CN106446563A - Incremental neural network model based constipation prediction method and system - Google Patents
Incremental neural network model based constipation prediction method and system Download PDFInfo
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Abstract
The invention discloses an incremental neural network model based constipation prediction method. The method includes the following steps: setting up a daily constipation data database; training a neural network model; acquiring daily life data prior to sending to a server, and storing the daily life data into a daily user data log sheet; extracting current day data from the daily user data log sheet, forming n-dimensional vectors, and performing normalization processing prior to inputting the data into a constipation pathology neural network model for constipation probability prediction; smart household constipation nursing equipment judging whether a constipation probability value is larger than 0.5 or not; when a user is judged to have constipation, the user going to hospital for examination by himself, transmitting examination results to the server through the smart household constipation nursing equipment, and the server judging whether the examination results are accurate or not; when the examination results are wrong, executing an incremental algorithm, and subjecting the neural network model to dynamic correction. Predication is accurate, and the neural network model is customized for each user.
Description
Technical field
The invention belongs to field of medical technology, more particularly to a kind of constipation based on increment type neural network model are predicted
Method and prognoses system.
Background technology
Current domestic each health management system arranged be respectively provided with constipation prediction and evaluation, the prediction mode which uses be.
Its principle is that by system matches fixed data and then personal lifestyle data entry system is shown ill probability.But due to human body and
The complexity, unpredictability of disease, in the form of expression of the bio signal with information, Changing Pattern (Self-variation and medical science
Change after intervention) on, which being detected and signal representation, all many-sides such as the data of acquisition and the analysis of information, decision-making are all
There is extremely complex non-linear relationship.So can only be the data examination of blindness using traditional Data Matching, it is impossible to judge
Logic association and variable between data and data, the codomain deviation for obtaining is big, causes the specificity of system prediction very poor,
So the current country is health management system arranged effectively cannot carry out Accurate Prediction to personal constipation.
Most of before this to constipation prediction be all using BP neural network model, but when new detection data is produced
Wait, it is necessary to train neural network model again, operation efficiency is extremely low.And after system user scale increases, server will
Training mission is completed in time cannot.
Content of the invention
The purpose of the present invention is that and overcomes the deficiencies in the prior art, there is provided a kind of based on increment type neural network model
Constipation Forecasting Methodology and prognoses system, the present invention by the neural network model training a large amount of patient in hospital pathological data of prediction,
Constipation pathology is found with constipation earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, this several diseases
Therefore the logic association and variable between, ultimately forms the constipation pathology neural network model to constipation illness probability Accurate Prediction,
The present invention is by gathering user's daily life data, and the periodicity of its data of active analysis, regularity are eventually through constipation pathology
Neural Network model predictive user suffers from constipation probability, reminds user's instant hospitalizing, work as neutral net in the way of visual effect
When model prediction is inaccurate, neural network model is constantly revised by increasable algorithm, training is set up for each equipment user
Go out the neural network model for the user, with the increase of use time, to set up the nerve net made to measure by the user
Network model, accuracy rate is greatly improved.
To achieve these goals, the invention provides a kind of constipation prediction side based on increment type neural network model
Method, comprises the steps：
Step (1), acquisition hospital's constipation etiology and pathology data source and the daily monitoring data of patient, daily so as to set up constipation
Data database；
Step (2), the daily data database of the constipation that is set up according to step (1) are off-line manner to neural network model
It is trained, with the constipation pathology neural network model for obtaining training；
Step (3), by intelligent monitoring device, the daily life data of user are acquired, and will collection daily life
Live data is sent to server, during server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, same day data are extracted, n-dimensional vector is formed, and n-dimensional vector is done
Constipation probabilistic forecasting is carried out in the constipation pathology neural network model for training in input step (2) after normalized, obtain
Constipation probability, server sends constipation probability to wired home constipation care appliances；
After step (5), the constipation probability of wired home constipation care appliances the reception server transmission, constipation probit is judged
Whether it is more than 0.5, if greater than 0.5, is then judged to that the user obtained constipation, attention device is warned so that user is reminded, if less than
0.5, then it is judged to that the user does not obtain constipation；
Step (6), when user is judged to constipation, user voluntarily removes examination in hospital, and inspection result is passed through intelligence
Energy family constipation care appliances send back server, and server judges whether inspection result is correct, if inspection result mistake,
Illustrate that constipation pathology Neural Network model predictive is inaccurate, if inspection result is correct, constipation pathology neutral net mould is described
Type prediction is accurate；
Step (7), when inspection result mistake, from the daily data logger of user extract m days in record preserve to
In incremental data table, when the record quantity in incremental data table is more than h bar, increasable algorithm is executed, to constipation pathology nerve
Network model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
Further, the input layer of neural network model is n node, and hidden layer number is that n*2+1, output layer is 1
Node, extracts k bar record from the daily data database table of constipation and is trained, and per bar, record is a n-dimensional vector, all numbers
According to using front elder generation through normalized so as to which then numerical value is executed following steps and neural network model is entered in [0,1] interval
Row training：
1) n-dimensional vector being input into neural network model, calculates all of weight vector in neural network model defeated to this
Enter the distance of n-dimensional vector, closest neuron is triumph neuron, and its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck
Domain；α (t) is learning rate, and it is the function for gradually successively decreasing with the increase of iterationses, and span is [01], through multiple
It is 0.62 that Optimal learning efficiency is chosen in experiment；DjIt is the distance of neuron j and triumph neuron；σ (t) is the letter for successively decreasing over time
Number；Iteration is all input to all input n-dimensional vectors in neural network model and is trained each time, when the iteration for reaching regulation
After number of times, neural network model training terminates.
Further, inspection result is sent back wired home constipation care appliances the form of the object information of server
For：{ checking whether correct, blood glucose value }, server judges whether inspection result is correct after object information is received.
Further, the increasable algorithm for carrying out dynamic corrections to constipation pathology neural network model is：
In incremental data table per bar vector V { V1,V2,…,Vn, it is sent in neural network model learning function
Row study, learning procedure is as follows：
1) first little random number is assigned to each weight vector of output layer and normalized is done, then using input mode vector V
Meansigma methodss Avg (V), be initialized as the weights of unique neuron in the 0th layer of neural network model, and be set to nerve of winning
Unit, calculates its quantization error QE；
2) 2 × 2 structures SOM are expanded out from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet that expands out in Layer layer, the power of this 4 neurons is initialized
Value；The input vector set Ci of i-th neuron is set to sky, main label is set to the main label ratio r of NULL, neuron ii
It is set to 0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) a vector VX is selected from VXiDo following judgement：
If VXiFor the data of not tape label, then its Euclidean distance with each neuron is calculated, chosen distance is most short
Neuron is used as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiThe identical and r of labeliThe maximum neuron of value is made
For triumph neuron, the triumph neuron main label is updated；
If can not find main label with VXiLabel identical neuron, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vector set W=W ∪ that wins
{VXi, calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training,
Go to step 4)；
6) quantization error QE of each neuron in the neural network model after calculating is adjustedi, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiFor the set that all input vectors for being mapped to neuron i are constituted；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall label data on neuron
Sum, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in the SOM, turn step
Rapid 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from the neuron, will
The subnet for newly growing increases in the subnet queue of Layer+1 layer；
If new neuron is not inserted in SOM also do not grow new subnet, illustrate that the subnet training is completed；
7) for all 2 × 2 structures SOM of the Layer+1 layer that newly expands out, iteration operating procedure 3)～5) to which again
It is trained, until neural network model no longer produces new neuron and new layering, whole training terminates.
Further, if user includes health check-up by other means and checks oneself, learn and constipation oneself is had suffered from, and intelligent family
The attention device of front yard constipation care appliances is not warned, then it represents that wired home constipation care appliances judge inaccurate, now execute
Step (6)～(7), wired home constipation care appliances are sent to object information on server.
Present invention also offers a kind of prognoses system of the constipation Forecasting Methodology, sets including intelligent monitoring device, intelligence
Standby data acquisition unit, server and wired home constipation care appliances, the intelligent monitoring device and the smart machine data
Harvester is connected, and the smart machine data acquisition unit is communicated with the server network by communication device one, the intelligence
Energy family constipation care appliances are communicated by communication device two and the server network.
Further, on the wired home constipation care appliances, attention device is provided with.
Further, the intelligent monitoring device include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent horse
Bucket and Intelligent light sensing equipment.
Beneficial effects of the present invention：
1st, the present invention is by the neural network model training a large amount of patient in hospital pathological data of prediction, find constipation pathology with just
Secret earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, the logic association between this several causes of disease
And variable, the constipation pathology neural network model to constipation illness probability Accurate Prediction is ultimately formed, the present invention is used by collection
Family daily life data, the periodicity of its data of active analysis, regularity are eventually through constipation pathology Neural Network model predictive
User suffers from constipation probability, reminds user's instant hospitalizing and prevention in the way of visual effect.
2nd, when Neural Network model predictive is inaccurate, neural network model is constantly revised by increasable algorithm, to be directed to
Each equipment user sets up the neural network model for training for the user, with the increase of use time, to set up to this
The neural network model that user makes to measure, accuracy rate is greatly improved.
Description of the drawings
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
Accompanying drawing to be used needed for technology description is had to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, acceptable
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the flow chart of the embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings invention is further illustrated, but be not limited to the scope of the present invention.
Embodiment
As shown in figure 1, a kind of constipation Forecasting Methodology based on increment type neural network model that the present invention is provided, including such as
Lower step：
Step (1), acquisition hospital's constipation etiology and pathology data source and the daily monitoring data of patient, daily so as to set up constipation
Data database；
Wherein daily monitoring data is 16 item data, and its 16 item data is age, sex, body fat, the amount of drinking water and frequency, greatly
Just number of times, color of defecating, body weight, defecate the time, digestion situation, proctalgia degree, time for falling asleep, daily smoking capacity, daily
Distance must be walked, daily the amount of drinking, 16 item data such as pursue an occupation, the present invention sets up 16 dimensional vectors with 16 item data；
Step (2), the daily data database of the constipation that is set up according to step (1) are off-line manner to neural network model
It is trained, with the constipation pathology neural network model for obtaining training；
Step (3), by intelligent monitoring device, the daily life data of user are acquired, and will collection daily life
Live data is sent to server, during server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, same day data are extracted, 16 dimensional vectors are formed, and to 16 dimensional vectors
Constipation probabilistic forecasting is carried out in the constipation pathology neural network model for training in input step (2) after doing normalized, obtain
To constipation probability, server sends constipation probability to wired home constipation care appliances；
After step (5), the constipation probability of wired home constipation care appliances the reception server transmission, constipation probit is judged
Whether it is more than 0.5, if greater than 0.5, is then judged to that the user obtained constipation, attention device is warned so that user is reminded, if less than
0.5, then it is judged to that the user does not obtain constipation；
Step (6), when user is judged to constipation, user voluntarily removes examination in hospital, and inspection result is passed through intelligence
Energy family constipation care appliances send back server, and server judges whether inspection result is correct, if inspection result mistake,
Illustrate that constipation pathology Neural Network model predictive is inaccurate, if inspection result is correct, constipation pathology neutral net mould is described
Type prediction is accurate；
Step (7), when inspection result mistake, from the daily data logger of user extract 7 days in record preserve to
In incremental data table, when the record quantity in incremental data table is more than 100, increasable algorithm is executed, to constipation pathology god
Dynamic corrections are carried out through network model；
Step (8), repeat step (3)～(7).
The input layer of the neural network model of the present invention is 16 nodes, and it is 1 node that hidden layer number is 33, output layer
(i.e. the probability of constipation), extracts 400000 records from the daily data database table of constipation and is trained, and per bar, record is one
Individual 16 dimensional vector, all data are using front elder generation through normalized so as to which then numerical value execute following walking in [0,1] interval
Suddenly neural network model is trained：
1) 16 dimensional vectors are input into neural network model, calculate all of weight vector in neural network model defeated to this
Enter the distance of 16 dimensional vectors, closest neuron is triumph neuron, and its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron neck
Domain；α (t) is learning rate, and it is the function for gradually successively decreasing with the increase of iterationses, and span is [0 1], through multiple
It is 0.62 that Optimal learning efficiency is chosen in experiment；DjIt is the distance of neuron j and triumph neuron；σ (t) is the letter for successively decreasing over time
Number；Iteration is all input to all input n-dimensional vectors in neural network model and is trained each time, when the iteration for reaching regulation
After number of times, neural network model training terminates.
The form of the object information that inspection result is sent back the wired home constipation care appliances of the present invention server is：
{ checking whether correct, blood glucose value }, server judges whether inspection result is correct after object information is received.
The increasable algorithm for dynamic corrections being carried out to constipation pathology neural network model of the present invention is：
In incremental data table per bar vector V { V1,V2,…,Vn, it is sent in neural network model learning function
Row study, learning procedure is as follows：
1) first little random number is assigned to each weight vector of output layer and normalized is done, then using input mode vector V
Meansigma methodss Avg (V), be initialized as the weights of unique neuron in the 0th layer of neural network model, and be set to nerve of winning
Unit, calculates its quantization error QE；
2) 2 × 2 structures SOM are expanded out from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet that expands out in Layer layer, the power of this 4 neurons is initialized
Value；The input vector set Ci of i-th neuron is set to sky, main label is set to the main label ratio r of NULL, neuron ii
It is set to 0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) a vector VX is selected from VXiDo following judgement：
If VXiFor the data of not tape label, then its Euclidean distance with each neuron is calculated, chosen distance is most short
Neuron is used as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiThe identical and r of labeliThe maximum neuron of value is made
For triumph neuron, the triumph neuron main label is updated；
If can not find main label with VXiLabel identical neuron, then find and VXiClosest neuron i makees
For triumph neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vector set W=W ∪ that wins
{VXi, calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training,
Go to step 4)；
6) quantization error QE of each neuron in the neural network model after calculating is adjustedi, neuronal messages entropy Ei
With the average quantization error MQE of subnet, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiFor the set that all input vectors for being mapped to neuron i are constituted；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall label data on neuron
Sum, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in the SOM, turn step
Rapid 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from the neuron, will
The subnet for newly growing increases in the subnet queue of Layer+1 layer；
If new neuron is not inserted in SOM also do not grow new subnet, illustrate that the subnet training is completed；
7) for all 2 × 2 structures SOM of the Layer+1 layer that newly expands out, iteration operating procedure 3)～5) to which again
It is trained, until neural network model no longer produces new neuron and new layering, whole training terminates.
If the user of the present invention includes health check-up by other means and checks oneself, learn and constipation oneself is had suffered from, and intelligent family
The attention device of front yard constipation care appliances is not warned, then it represents that wired home constipation care appliances judge inaccurate, now execute
Step (6)～(7), wired home constipation care appliances are sent to object information on server.
Present invention also offers a kind of prognoses system of the constipation Forecasting Methodology, sets including intelligent monitoring device, intelligence
Standby data acquisition unit, server and wired home constipation care appliances, the intelligent monitoring device and the smart machine data
Harvester is connected, and the smart machine data acquisition unit is communicated with the server network by communication device one, the intelligence
Energy family constipation care appliances are communicated by communication device two and the server network.
Attention device is provided with the wired home constipation care appliances of the present invention.
The intelligent monitoring device of the present invention include Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool
With Intelligent light sensing equipment etc..
The present invention finds constipation pathology and constipation by a large amount of patient in hospital pathological data of neural network model training prediction
Earlier life variations in detail, clinical symptoms, examination criteria value, high-risk group's feature, the logic association between this several causes of disease and
Variable, ultimately forms the constipation pathology neural network model to constipation illness probability Accurate Prediction, and the present invention is by gathering user
Daily life data, the periodicity of its data of active analysis, regularity are used eventually through constipation pathology Neural Network model predictive
Family suffer from constipation probability, remind user's instant hospitalizing and prevention in the way of visual effect.
During all data of the present invention are preserved to server, calculating cost can be significantly saved, hardware configuration is low, so as to sell
Valency is also low.
The present invention carries communication device one and communication device two, can pass through wifi from the Internet that is dynamically connected, and protect for a long time
Hold online.Various intelligent monitoring devices can easily access present device by modes such as network or bluetooths, set in acquisition
The daily life data of the monitoring of intelligent monitoring device, data that therefore present device obtain are uploaded automatically after standby mandate can
It is real-time, accurate, polynary.
As everyone physical trait is different, the data characteristicses for being shown during constipation morbidity also can be different.Therefore
Conventional is not high by the method accuracy rate of neural network prediction constipation.The present invention sets up for each equipment user and trains pin
Neural network model to the user, in operation after a period of time, makes neural network prediction by producing to measure to the user
Model, accuracy rate is greatly improved.
When neural network model is judged by accident, error message will be feedbacked to server, pin by wired home constipation care appliances
To user's dynamic corrections neural network model, when similar characteristics data occurs in the next user, will not judge by accident again.Cause
This, with the increase of use time, the judgement of the wired home constipation care appliances of the present invention will be more and more accurate.
Ultimate principle, principal character and the advantages of the present invention of the present invention has been shown and described above.The technology of the industry
Personnel it should be appreciated that the present invention is not restricted to the described embodiments, simply explanation described in above-described embodiment and description this
The principle of invention, without departing from the spirit and scope of the present invention the present invention also have various changes and modifications, these change
Change and improvement is both fallen within scope of the claimed invention.The claimed scope of the invention by appending claims and its
Equivalent is defined.
Claims (8)
1. a kind of constipation Forecasting Methodology based on increment type neural network model, it is characterised in that comprise the steps：
Step (1), obtain hospital constipation and cure the disease etiology and pathology data source and the daily monitoring data of patient, daily so as to set up constipation
Data database；
Step (2), the daily data database of the constipation that is set up according to step (1) are carried out to neural network model off-line manner
Training, with the constipation pathology neural network model for obtaining training；
Step (3), by intelligent monitoring device, the daily life data of user are acquired, and will collection daily life number
According to transmission to server, during server preserves the daily life data of user to the daily data logger of user；
Step (4), from the daily data logger of user, same day data are extracted, n-dimensional vector is formed, and normalizing is done to n-dimensional vector
Constipation probabilistic forecasting is carried out in the constipation pathology neural network model for training in input step (2) after change process, obtain constipation
Probability, server sends constipation probability to wired home constipation care appliances；
After step (5), the constipation probability of wired home constipation care appliances the reception server transmission, whether constipation probit is judged
More than 0.5, if greater than 0.5, then it is judged to that the user obtained constipation, attention device is warned so that user is reminded, if less than 0.5,
Then it is judged to that the user does not obtain constipation；
Step (6), when user is judged to constipation, user voluntarily removes examination in hospital, and by inspection result by intelligent family
Front yard constipation care appliances send back server, and server judges whether inspection result is correct, if inspection result mistake, illustrates
Constipation pathology Neural Network model predictive is inaccurate, if inspection result is correct, illustrates that constipation pathology neural network model is pre-
It is accurate to survey；
Step (7), when inspection result mistake, from the daily data logger of user extract m days in record preserve to increment
In tables of data, when the record quantity in incremental data table is more than h bar, increasable algorithm is executed, to constipation pathology neutral net
Model carries out dynamic corrections；
Step (8), repeat step (3)～(7).
2. a kind of constipation Forecasting Methodology based on increment type neural network model according to claim 1, it is characterised in that
The input layer of neural network model is n node, and it is 1 node that hidden layer number is n*2+1, output layer, from constipation day constant
It is trained according to k bar record is extracted in database table, per bar, record is a n-dimensional vector, and all data are using front elder generation through returning
One change is processed so as to which then numerical value is executed following steps and neural network model is trained in [0,1] interval：
1) it is input into a n-dimensional vector and neural network model is arrived, in calculating neural network model, all of weight vector is to input n
The distance of dimensional vector, closest neuron is triumph neuron, and its computing formula is as follows：
Wherein：WkIt is the weight vector of triumph neuron, | | ... | | for Euclidean distance；
2) weight vector of the neuron in adjustment triumph neuron and triumph neuron field, formula is as follows：
Wherein：WjT () is neuron；Wj(t+1) weight vector before being adjustment and after adjustment；J belongs to triumph neuron field；α
T () is learning rate, it is the function for gradually successively decreasing with the increase of iterationses, and span is [0 1], through many experiments
It is 0.62 to choose Optimal learning efficiency；DjIt is the distance of neuron j and triumph neuron；σ (t) is the function for successively decreasing over time；
Iteration is all input to all input n-dimensional vectors in neural network model and is trained each time, when the iteration time for reaching regulation
After number, neural network model training terminates.
3. a kind of constipation Forecasting Methodology based on increment type neural network model according to claim 1, it is characterised in that
The form of the object information that inspection result is sent back wired home constipation care appliances server is：Correct, blood is checked whether {
Sugar value }, server judges whether inspection result is correct after object information is received.
4. a kind of constipation Forecasting Methodology based on increment type neural network model according to claim 1, it is characterised in that
The increasable algorithm for dynamic corrections being carried out to constipation pathology neural network model is：
In incremental data table per bar vector V { V1,V2,…,Vn, it is sent in neural network model learning function and is learned
Practise, learning procedure is as follows：
1) first little random number is assigned to each weight vector of output layer and normalized is done, then using the flat of input mode vector V
Average Avg (V), is initialized as the weights of unique neuron in the 0th layer of neural network model, and is set to triumph neuron, meter
Calculate its quantization error QE；
2) 2 × 2 structures SOM are expanded out from the 0th layer of neuron, and its level identities Layer is set to 1；
3) for each 2 × 2 structure SOM subnet that expands out in Layer layer, the weights of this 4 neurons are initialized；Will
The input vector set Ci of i-th neuron is set to sky, and main label is set to the main label ratio r of NULL, neuron iiIt is set to
0；The abnormity early warning data vector V of new SOM inherits the triumph input vector set VX of his father's neuron；
4) a vector VX is selected from VXiDo following judgement：
If VXiFor the data of not tape label, then calculate its Euclidean distance with each neuron, the most short nerve of chosen distance
Unit is used as triumph neuron；
If VXiFor the data of tape label, then select main label and VXiThe identical and r of labeliThe neuron of value maximum is used as obtaining
Victory neuron, updates the triumph neuron main label；
If can not find main label with VXiLabel identical neuron, then find and VXiClosest neuron i is used as obtaining
Victory neuron；
5) weights of neuron in triumph neuron and its neighborhood are adjusted, update the vector set W=W ∪ { VX that winsi,
Calculate main label, the main label ratio r of triumph neuroniWith comentropy EiIf. not up to predetermined frequency of training, go to step
4)；
6) quantization error QE of each neuron in the neural network model after calculating is adjustedi, neuronal messages entropy EiAnd son
The average quantization error MQE of net, formula is as follows：
Wherein：WiFor the weight vector of neuron i, CiFor the set that all input vectors for being mapped to neuron i are constituted；
Wherein：niRepresent to fall that label is the number of samples of i on neuron, m represents to fall the total of label data on neuron
Number, T represents to fall the sample label species set on neuron；
Then judge：
If MQE>QE × threshold value q of father node, wherein q=0.71, then insert a line neuron in the SOM, go to step 4)；
If Ei>The E of father nodei× threshold value p, wherein p=0.42, then grow one layer of new subnet from the neuron, will be newly long
The subnet for going out increases in the subnet queue of Layer+1 layer；
If new neuron is not inserted in SOM also do not grow new subnet, illustrate that the subnet training is completed；
7) for all 2 × 2 structures SOM of the Layer+1 layer that newly expands out, iteration operating procedure 3)～5) which is re-started
Training, until neural network model no longer produces new neuron and new layering, whole training terminates.
5. a kind of constipation Forecasting Methodology based on increment type neural network model according to claim 1, it is characterised in that
If user includes health check-up by other means and checks oneself, learn and constipation oneself is had suffered from, and wired home constipation care appliances
Attention device is not warned, then it represents that wired home constipation care appliances judge inaccurate, now execution step (6)～(7), intelligence
Family's constipation care appliances are sent to object information on server.
6. the prognoses system of constipation Forecasting Methodology described in a kind of employing claim 1～6, it is characterised in that including intelligent monitoring
Equipment, smart machine data acquisition unit, server and wired home constipation care appliances, the intelligent monitoring device and the intelligence
Energy device data acquisition device is connected, and the smart machine data acquisition unit is logical with the server network by communication device one
News, the wired home constipation care appliances are communicated with the server network by communication device two.
7. the prognoses system of constipation Forecasting Methodology according to claim 7, it is characterised in that the wired home constipation nursing
Attention device is provided with equipment.
8. the prognoses system of constipation Forecasting Methodology according to claim 7, it is characterised in that the intelligent monitoring device includes
Intelligent worn device, Intelligent water cup, Intelligent weight claim, intelligent closestool and Intelligent light sensing equipment.
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