CN103234610B - Weighing method applicable to truck scale - Google Patents
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Abstract
The invention discloses a weighing method applicable to a truck scale, and the method comprises a weighing sensor, a data acquisition device, a microprocessor and a display which are used, and the following steps that a weighing mathematical model is established, a weighing signal is acquired and on-line weighing is carried out; the step of establishing the weighing mathematical model comprises an ideal weighing model, an actual weighing model and the training methods thereof; the ideal weighing model is a linear function; the actual weighing model is a three-layer BP neural network, wherein a first layer is an input layer, a second layer is a hidden layer and a third layer is an output layer; before on-line weighing, the training of setting sample sizes must be carried out on the ideal weighing model and the actual weighing model; the training is carried out when the microprocessor is connected with an external computer; sample information of set quantity is acquired; the ideal weighing model and a derivative thereof serve as constraint conditions; finally the parameters W, b (1), V and b (2) of the actual weighing model are acquired and saved in the microprocessor; and then the external computer is removed.
Description
Technical field
The present invention relates to a kind of Weighing method being applicable to truck scale.
Technical background
Truck scale is the important branch of weighing apparatus, is mainly used in bulk supply tariff metering.Current analog electronic vehicle weighing apparatus occupies the leading position in truck scale market, and it is primarily of load-bearing force transmission mechanism (scale body), simulation LOAD CELLS, the large master unit composition of weighting display instrument three.Truck scale is different according to range, generally has 4 ~ 12 tunnel LOAD CELLS.These sensors, according to certain topological structure, are distributed in below scale body symmetrically, constitute a multisensor syste.There is coupling in this multisensor syste, each road sensor exports interrelated, and relevant with load loading position.Truck scale is concentrated cumulative in the output signal of simulation connection He Zhongjiangge road LOAD CELLS, obtain a voltage signal proportional with tested quality of loads, after signal condition, A/D conversion, obtain weighing results by single-chip microcomputer process, send display, communication, complete weighing of tested load.Uneven loading error and linearity error are two principal elements affecting truck scale weighing results accuracy.Uneven loading error is because truck scale is by the impact of various non-linear factor, and when tested load is in diverse location on truck scale loading end, weighing results is inconsistent and the error that produces; Linearity error is because the characteristic of each road LOAD CELLS is inconsistent, causes the constrained input of truck scale and nonideal linear relationship, thus the weighting error produced.The uneven loading error of existing truck scale is what to separate with linearity error compensation process, traditional uneven loading error compensation method is by resistor in the artificial weighing apparatus of vehicles repeatedly terminal box, change the sensor passage gain of every road, compensate uneven loading error, this method manual operation is loaded down with trivial details, inefficiency, compensation effect is poor, for this reason, scholar is had to propose multiple linear regression analysis method (" research of large-scale weighing machine system partial load digitized compensation method ", Chen Chang, Wang Xiaoliang, Qin Zijun, Journal of Dalian University of Technology Total, 1994, 1), utilize the method for Solving Linear angular difference correction factor (" studying based on the intelligent weighing sensor of advance data treatment technology ", Zhu Zijian, Nanjing Aero-Space University's PhD dissertation, 2005), but these methods can not solve each sensor output relevance problem because topological structure brings, the various non-linear factor impact of truck scale is not considered yet, therefore compensation effect is poor, have scholar to adopt neural net method to carry out truck scale uneven loading error and linearity error compensate (" the truck scale error compensation based on multi-sensor information fusion ", vast stretch of wooded country army, Teng Zhaosheng, Chi Hai, etc., Chinese journal of scientific instrument, 2009,6, " the truck scale error compensation based on multiple RBF neural network ", vast stretch of wooded country army, Teng Zhaosheng, Chi Hai, etc., Hunan University's journal, 2010,5), although can greatly reduce weighting error, neural network needs a large amount of training samples, and workload is large, the main cause that workload is large is that truck scale range is large, and the standard test weight needed during test is many, load(ing) point is many, and weighing information obtains not easily.It is after uneven loading error has compensated that existing truck scale linearity error compensates, profit completes with the following method: first utilize the standard test weight of Different Weight to be carried in successively on truck scale body, obtain target weighing results and actual weighing results, then actual weighing results is doubly taken advantage of correction factor, make it to equal target weighing results.This method is based on truck scale input-output on the basis being linear relationship, and differ comparatively large with reality, therefore compensation effect is poor.
Summary of the invention
The object of the invention is to overcome deficiency of the prior art, provide a kind of Weighing method being applicable to truck scale newly: namely utilize the Nonlinear Function Approximation ability that neural network is good, the actual weighting model of structure truck scale; Utilize the constraint condition of the desirable weighting model constructing neural network of truck scale simultaneously, to reduce the sample needed for neural metwork training, reduce workload, complete the optimization of the actual weighting model of truck scale, finally realize truck scale correct amount and error compensation.
Object of the present invention is achieved by following technical proposals:
Described Weighing method comprises use LOAD CELLS, data collector, microprocessor and display; Described LOAD CELLS is connected with microprocessor by data collector; Described display is connected with microprocessor; The step of described Weighing method comprises foundation and to weigh mathematical model, weighing-up wave collection, online weighing; The weigh mathematical model of mathematical model of described foundation comprises desirable weighting model, actual weighting model and their training method, its step:
1) described desirable weighting model is linear function; Being input as of described linear function
nthe data that road LOAD CELLS exports
, export as A (X); Its Input output Relationship expression formula is formula (1):
（1）；
In formula,
p i for the gain coefficient of LOAD CELLS, its value obtains by carrying out training to desirable weighting model;
2) described actual weighting model is three layers of BP neural network, and ground floor is input layer, and the second layer is hidden layer, and third layer is output layer, and their network structure is as follows respectively:
The neuronal quantity of input layer
nfor the number of LOAD CELLS;
The neuronal quantity of hidden layer
m=
, in formula:
k=1 ~ 10 is correction factor;
lfor the neuronal quantity of output layer; Hidden layer excitation function adopts Log-Sigmoid function, namely exports
for formula (2):
（2）；
The neuronal quantity of output layer
lit is 1; Output layer excitation function adopts linear function; The neural network of output layer exports
for formula (3):
（3）；
In formula,
wfor neural network input layer is to the weight matrix of hidden layer,
b (1)for hidden layer bias vector,
vfor hidden layer is to the weight vector of output layer, b
(2)for output layer bias,
xfor neural network input vector,
w mi for input layer
iroad is input to of hidden layer
mindividual neuronic connection weights,
for hidden layer
mindividual neuronic bias,
v m for hidden layer
mindividual neuron to the connection weights of output layer,
x(
i) be input layer
iroad inputs;
3) before truck scale drops into online weighing, the training of set point number must be carried out to desirable weighting model and actual weighting model, training process carries out when microprocessor is connected with outer computer, with desirable weighting model and derivative thereof for constraint condition, finally obtain actual weighting model parameter
w,
b (1),
vand b
(2)preserve in the microprocessor, then withdraw outer computer; Special training software is installed in outer computer;
The step of training actual weighting model is as follows:
I) gather training sample: the standard test weight preparing some, each standard test weight quality is different, by the standard test weight random loading of different quality on truck scale body,
nroad sensor just has
nindividual output data,
nindividual output data and corresponding standard test weight quality form one group of training sample and are kept in outer computer;
II) construct training objective function, its relational expression is formula (4):
（4）；
III) ask the derivative of the desirable weighting model of truck scale, its relational expression is formula (5):
（5）；
IV) ask the weight coefficient of desirable weighting model in training objective function, its relational expression is formula (6):
（6）；
V) ask third layer output layer
derivative, its relational expression is formula (7):
（7）；
VI) ask respectively
w, b (1) , Vand b
(2)increment
,
,
,
, and right
w, b (1) , Vand b
(2)upgrade, their relational expression is respectively formula (8), (9):
（8）
（9）
In formula (9),
,
,
,
be respectively
,
,
,
value after renewal,
,
,
,
be respectively
,
,
,
value before renewal;
Vii) arrange training starting condition, carry out according to formula (6) ~ (9) training setting quantity, the error amount that training is produced, in setting range, obtains the weight matrix of input layer to hidden layer respectively
w, hidden layer bias vector
b (1), hidden layer is to the weight vector of output layer
v, output layer bias b
(2)end value, and be kept in the storage element of microprocessor, for called during online weighing.
In the training process to desirable weighting model, first utilize the training sample gathered in actual weighting model training process, then utilize least square method to train, obtain final model coefficient
p i , its relational expression is formula (10):
（10）
In formula (10),
pserve as reasons
p i vector, namely
p=[
p 1,
p 2...,
p n ]
t,
nfor the number of LOAD CELLS;
x=[
x 1,
x 2...,
x j...,
x k ] be input amendment matrix,
y=[
y 1,
y 2,
y j ...,
y k ].
Described online weighing should carry out after actual weighting model training is qualified, and its step is as follows:
1) by gather
nthe weighing-up wave of road LOAD CELLS is as the input vector of BP neural network ground floor
x;
2) by input vector
xwith the actual weighting model parameter of preserving in the memory unit
w,
b (1),
vand b
(2)substitute into together in formula (3), try to achieve BP neural network and export
for final weighing results;
3) display shows final weighing results.
The method that described weighing-up wave gathers, the data obtained after the output signal of each road LOAD CELLS is carried out signal amplification, filtering and analog-to-digital conversion process, train the input vector with online weighing as desirable weighting model and actual weighting model
x.
Described microprocessor is single-chip microcomputer, dsp processor or other embedded system devices, and with storage unit.
Compared with the prior art the present invention has following advantage: the present invention can realize the automatic Weighing of truck scale, and carries out the auto-compensation of uneven loading error and linearity error simultaneously, substantially increases the accuracy of weighing results; Decrease the sample size needed for the training of truck scale weighting model simultaneously, improve work efficiency.
Accompanying drawing explanation
Fig. 1 is the FB(flow block) that the present invention trains embodiment.
Fig. 2 is the FB(flow block) of online weighing embodiment of the present invention.
Fig. 3 is neural network embodiment of the present invention, wherein,
f1 is hidden layer excitation function,
f2 is output layer excitation function.
Fig. 4 is the signal acquisition circuit theory diagram of one embodiment of the invention.
Fig. 5 is embodiments of the invention 5 truck scale online weighing and error compensation simulation result figure, and wherein (a) compensates forward and backward weighing results comparison diagram, and (b) compensates forward and backward weighing results graph of errors comparison diagram.
In the diagram: 1-modulate circuit, 2-analog to digital conversion circuit, 3-microprocessor, 4-power module, 5-outer computer, 6-keyboard, 7-display, 8-communication interface.
Embodiment
Below in conjunction with drawings and Examples, the invention will be further described:
See Fig. 1-5, described Weighing method comprises use LOAD CELLS, data collector, microprocessor 3 and display 7; Described LOAD CELLS is connected with microprocessor by data collector; Described display is connected with microprocessor; The step of described Weighing method comprises foundation and to weigh mathematical model, weighing-up wave collection, online weighing; The weigh mathematical model of mathematical model of described foundation comprises desirable weighting model, actual weighting model and their training method, its step;
1) described desirable weighting model is linear function; Being input as of described linear function
nthe data that road LOAD CELLS exports
, export as A (X); Its Input output Relationship expression formula is formula (1):
（1）；
In formula,
p i for the gain coefficient of LOAD CELLS, its value obtains by carrying out training to desirable weighting model;
2) described actual weighting model is three layers of BP neural network, and ground floor is input layer, and the second layer is hidden layer, and third layer is output layer, and their network structure is as follows respectively:
The neuronal quantity of input layer
nfor the number of LOAD CELLS;
The neuronal quantity of hidden layer
m=
, in formula:
k=1 ~ 10 is correction factor;
lfor the neuronal quantity of output layer; Hidden layer excitation function adopts Log-Sigmoid function, namely exports
for formula (2):
（2）；
The neuronal quantity of output layer
lit is 1; Output layer excitation function adopts linear function; The neural network of output layer exports
for formula (3):
（3）；
In formula,
wfor neural network input layer is to the weight matrix of hidden layer,
b (1)for hidden layer bias vector,
vfor hidden layer is to the weight vector of output layer, b
(2)for output layer bias,
xfor neural network input vector,
w mi for input layer
iroad is input to of hidden layer
mindividual neuronic connection weights,
for hidden layer
mindividual neuronic bias,
v m for hidden layer
mindividual neuron to the connection weights of output layer,
x(
i) be input layer
iroad inputs;
3) before truck scale drops into online weighing, the training of set point number must be carried out to desirable weighting model and actual weighting model, training process carries out when microprocessor is connected with outer computer, with desirable weighting model and derivative thereof for constraint condition, finally obtain actual weighting model parameter
w,
b (1),
vand b
(2)preserve in the microprocessor, then withdraw outer computer; Special training software is installed in outer computer;
The step of training actual weighting model is as follows:
I) gather training sample: the standard test weight preparing some, each standard test weight quality is different, by the standard test weight random loading of different quality on truck scale body,
nroad sensor just has
nindividual output data,
nindividual output data and corresponding standard test weight quality form one group of training sample and are kept in outer computer;
II) construct training objective function, its relational expression is formula (4):
（4）；
III) ask the derivative of the desirable weighting model of truck scale, its relational expression is formula (5):
（5）；
IV) ask the weight coefficient of desirable weighting model in training objective function, its relational expression is formula (6):
（6）；
V) ask third layer output layer
derivative, its relational expression is formula (7):
（7）；
VI) ask respectively
w, b (1) , Vand b
(2)increment
,
,
,
, and right
w, b (1) , Vand b
(2)upgrade, their relational expression is respectively formula (8), (9):
（8）
（9）
In formula (9),
,
,
,
be respectively
,
,
,
value after renewal,
,
,
,
be respectively
,
,
,
value before renewal;
Vii) arrange training starting condition, carry out the training of set point number according to formula (6) ~ (9), the error amount that training is produced, in setting range, obtains the weight matrix of input layer to hidden layer respectively
w, hidden layer bias vector
b (1), hidden layer is to the weight vector of output layer
v, output layer bias b
(2)end value, and be kept in the storage element of microprocessor, for called during online weighing.
In the training process to desirable weighting model, first utilize the training sample gathered in actual weighting model training process, then utilize least square method to train, obtain final model coefficient
p i , its relational expression is formula (10):
（10）
In formula (10),
pserve as reasons
p i vector, namely
p=[
p 1,
p 2...,
p n ]
t,
nfor the number of LOAD CELLS;
x=[
x 1,
x 2...,
x j...,
x k ] be input amendment matrix,
y=[
y 1,
y 2,
y j ...,
y k ].
Described online weighing should carry out after actual weighting model training is qualified, and its step is as follows:
1) by gather
nthe weighing-up wave of road LOAD CELLS is as the input vector of BP neural network ground floor
x;
2) by input vector
xwith the actual weighting model parameter of preserving in the memory unit
w,
b (1),
vand b
(2)substitute into together in formula (3), try to achieve BP neural network and export
for final weighing results;
3) display 7 shows final weighing results.
The method that described weighing-up wave gathers, the data obtained after the output signal of each road LOAD CELLS is carried out signal amplification, filtering and analog-to-digital conversion process, train the input vector with online weighing as desirable weighting model and actual weighting model
x.
Described microprocessor is single-chip microcomputer, dsp processor or other embedded system devices, and with storage unit.
Described data collector comprises modulate circuit 1 and analog to digital conversion circuit 2, adopts known technology.
Embodiment 1:
Figure 4 shows that the major constituents being applicable to truck scale weighing section of the present invention: the output terminal connection data harvester of each LOAD CELLS, data collector comprises modulate circuit 1 and analog to digital conversion circuit 2; Weighing-up wave carries out through amplifying in modulate circuit 1, filtering process, and carry out being converted to digital signal through analog to digital conversion circuit 2, digital data transmission is to microprocessor 3; Microprocessor also configures power module 4, keyboard 6, display 7 and communication interface 8; Power module 4 is the power supplies such as modulate circuit 1, analog to digital conversion circuit 2, microprocessor 3.During training, microprocessor 3 connects outer computer 5 by communication interface 8; During online weighing, outer computer 5 is withdrawn.
Embodiment 2: the method for actual weighting model training and step.
In the present embodiment, truck scale have 8 tunnel LOAD CELLS (
n=8), range is 40 tons, and the max cap. of every road LOAD CELLS is 20 tons, and the number of divisions is 4000, verification scale interval
eand actual graduation value
dbe 10kg; Microprocessor 3 adopts the High Performance SCM MSP430F449 of TI company.See Fig. 1, the step of training actual weighting model is as follows:
(1) desirable weighting model is constructed, the constraint condition as True k Scale Weighing System: with
nthe weighing-up wave that road LOAD CELLS exports
for input, A (X) are for exporting, the desirable weighting model of structure truck scale, as shown in Equation (1);
(2) structure is based on the actual weighting model of truck scale of neural network: with No. 8 sensor weighing-up waves be input, with truck scale weighing results for output, construct three layers of BP neural network that one 8 input 1 exports, as the actual weighting model of truck scale, its structure as shown in Figure 3.The hidden layer neuron number of this neural network
mmeet
,
kget 1 ~ 10.
kcan be determined by following methods: first make
k=1, calculate the error of neural network, if error meets the demands or minimum, then determine
knumerical value; Otherwise,
kadd 1, recalculate the error of neural network, until meet the demands or error minimum.By test of many times, the present embodiment is determined
k=2, i.e. hidden layer neuron number
m=5.In Fig. 3,
for of input layer
iindividual neuron is to of hidden layer
mneuronic weights;
,
,
be respectively the 1st of hidden layer the, 2,
mindividual neuronic bias;
for output layer is to hidden layer
mindividual neuronic weights; b
(2)for output layer bias;
,
,
be respectively the 1st of hidden layer the, 2,
mindividual neuronic output;
f 1for hidden layer excitation function, it adopts Log-Sigmoid function; Output layer excitation function adopts linear function, and therefore network exports
represent with formula (3);
(3) objective function of actual weighting model training is constructed: with the desirable weighting model of truck scale and derivative thereof for constraint condition, structure training sample objective function, represents with formula (4);
(4) standard test weight is loaded, gather weighing-up wave, form training and testing sample: the standard test weight utilizing the different tonnages such as 0.5 ton, 1 ton, 3 tons, 6 tons, 12 tons, 18 tons, 24 tons, 36 tons, be carried in the diverse location of truck scale body respectively, system is by LOAD CELLS, modulate circuit 1, analog to digital conversion circuit 2 and microprocessor 3, gather 50 group of 8 road load cell signal, obtain 50 groups of samples
, as the formula (11), wherein 30 groups as train samples, and 20 groups are used for neural network test sample book.These samples are sent to outer computer 5 by communication interface 8 and preserve by microprocessor 3, and for neural network off-line training is prepared, computing machine here refers to outer computer;
（11）
(5) coefficient of desirable weighting model is asked: utilize formula (10) to ask the coefficient of desirable weighting model
p i ;
(6) setting training initial parameter: target square error MSE is 0.0000000001, learning rate
ηbe 0.8, coefficient
μ j determine online, hidden neuron number
mbe 5, frequency of training is 10000.
(7) get training sample, neural network training, regulate neural parameter
w,
b (1),
vand b
(2): from the sample set be kept at outer computer, take out training sample, and utilize special training software to train, utilize formula (9) to adjust parameter simultaneously
w,
b (1),
vand b
(2); Special training software adopts the training method shown in formula (4) ~ (10), utilizes MATLAB and the exploitation of Virtual Instrument LabVIEW Platform Designing, other programming languages also can be utilized to realize.
(8) enter determining program " training completes ", if completed, obtain the qualification parameters of actual weighting model
w,
b (1),
vand b
(2); Otherwise, return step and " get training sample " and restart next round training;
(9) outer computer by communication interface by actual weighting model parameter downloads in microprocessor, and preserve in the memory unit, for online weighing is prepared; Withdraw outer computer, i.e. the connection of disconnecting external computing machine and microprocessor simultaneously, terminate training.
According to neural network design theory (" neural network design ", the work such as Martin T. Hagan, Dai Kuiyi, China Machine Press, 2005,8), according to traditional neural network training method (namely not utilizing the desirable weighting model of truck scale), at least need training sample number Num=(
m+ 1) *
n+ (
m+ 1) *
l, in formula,
mfor hidden neuron number,
ninput layer number,
lfor output layer number.In the present embodiment,
m=5,
n=8,
l=1, therefore Num=54, namely at least need 54 groups of training samples, otherwise larger extensive error can be produced, thus cause weighting model unavailable.But because the desirable weighting model that present invention utilizes truck scale is as priori, only make use of 30 groups of training samples can meet the demands, and training sample number far fewer than 54 groups, thus decreases workload.
Embodiment 3: online weighing.
In the present embodiment, truck scale have 8 tunnel LOAD CELLS (
n=8), range is 40 tons, and the max cap. of every road LOAD CELLS is 20 tons, and the number of divisions is 4000, verification scale interval
eand actual graduation value
dbe 10kg; Microprocessor 3 adopts the High Performance SCM MSP430F449 of TI company, utilizes in embodiment 2 and has trained qualified actual weighting model to carry out online weighing.See Fig. 2, online weighing step is as follows:
(1) tested load loads: namely truck is carried on truck scale body optional position, carries out weighing-up wave collection after stable;
(2) microcontroller acquires
nthe weighing-up wave of road LOAD CELLS: system, by LOAD CELLS, modulate circuit 1, analog to digital conversion circuit 2 and microprocessor 3, gathers 8 tunnel LOAD CELLS output signals
x i (
i=1,2 ..., 8), as the input vector X of actual weighting model, namely X=[
x 1,
x 2...,
x 8];
(3) call
w,
b (1),
vand b
(2): the parameter calling actual weighting model from storage unit
w,
b (1),
vand b
(2);
(4) output of actual weighting model is calculated: calculate the output obtaining neural network according to formula (3), this output is the final weighing results of truck scale after error compensation;
(5) show weighing results over the display, and terminate this online weighing.
The truck scale adopting the method for the invention after tested after, obtain weighing and error compensation effect as shown in Figure 5.Fig. 5 (a) carries out the forward and backward output weighing results of online weighing and error compensation for adopting the method, and Fig. 5 (b) be that the error adopting the method to compensate front and back contrasts.As can be seen from Fig., adopt the truck scale weighing results of the method for the invention accurate, error compensation is effective.
The concerned countries standard utilizing truck scale to examine and determine, to adopting the truck scale of the method for the invention to carry out on-the-spot test, table 1 is uneven loading error verification result, and table 2 is linearity error verification result.The parameter of truck scale: 8 tunnel LOAD CELLS (
n=8), range is 40 tons, the max cap. of LOAD CELLS is 20 tons, the number of divisions is 4000, verification scale interval
eand actual graduation value
dbe 10kg.
table 1 uneven loading error verification result
| Position | 1# | 2# | 3# | 4# | 5# | 6# | 7# | 8# |
| Error E(kg) | -7 | 2 | -5 | -6 | -8 | 7 | 4 | -5 |
| Permissible error (kg) | ±10 | ±10 | ±10 | ±10 | ±10 | ±10 | ±10 | ±10 |
table 2 linearity error verification result
| Weighing range (t) | 0~0.2 | 0.2~5 | 5~10 | 10~20 | 20~40 |
| Measurement error (kg) | +3 | -5 | -8 | -9 | +12 |
| Permissible error (kg) | ±5 | ±5 | ±10 | ±10 | ±15 |
In table 1,1# represents No. 1 loading area (i.e. region, LOAD CELLS position), other have same meaning.As can be seen from table 1,2, the truck scale uneven loading error adopted in this way is less than the permissible error that concerned countries standard specifies, linearity error is less than the permissible error that concerned countries standard specifies equally, and error compensation is respond well.
Above embodiment is not as a limitation of the invention.
Claims (5)
1. be applicable to a Weighing method for truck scale, described Weighing method uses LOAD CELLS, data collector, microprocessor and display; Described LOAD CELLS is connected with microprocessor by data collector; Described display is connected with microprocessor; Described Weighing method comprises foundation and to weigh mathematical model, weighing-up wave collection, online weighing; It is characterized in that: the weigh mathematical model of mathematical model of described foundation comprises desirable weighting model, actual weighting model, and its step is as follows;
1) described desirable weighting model is linear function; Being input as of described linear function
nthe data that road LOAD CELLS exports
x (
i)
, export as A (X); Its Input output Relationship expression formula is formula (1):
（1）；
In formula,
p i for the gain coefficient of LOAD CELLS, its value obtains by carrying out training to desirable weighting model;
2) described actual weighting model is three layers of BP neural network, and ground floor is input layer, and the second layer is hidden layer, and third layer is output layer, and their network structure is as follows respectively:
The neuronal quantity of input layer
nfor the number of LOAD CELLS;
The neuronal quantity of hidden layer
, in formula:
k=1 ~ 10 is correction factor;
lfor the neuronal quantity of output layer; Hidden layer excitation function adopts Log-Sigmoid function, namely exports
a m (1)for formula (2):
（2）；
The neuronal quantity of output layer
lit is 1; Output layer excitation function adopts linear function; The neural network of output layer exports
for formula (3):
（3）；
In formula,
wfor neural network input layer is to the weight matrix of hidden layer,
b (1)for hidden layer bias vector,
vfor hidden layer is to the weight vector of output layer,
b (2)for output layer bias,
xfor neural network input vector,
w mi for input layer
iroad is input to of hidden layer
mindividual neuronic connection weights,
b m (1)for hidden layer
mindividual neuronic bias,
v m for hidden layer
mindividual neuron to the connection weights of output layer,
x(
i) be input layer
iroad inputs;
3) before truck scale drops into online weighing, the training of set point number must be carried out to desirable weighting model and actual weighting model, training process carries out when microprocessor is connected with outer computer, with desirable weighting model and derivative thereof for constraint condition, finally obtain actual weighting model parameter
w,
b (1),
vwith
b (2)preserve in the microprocessor, then withdraw outer computer; Special training software is installed in outer computer;
The step of training actual weighting model is as follows:
I) gather training sample: the standard test weight preparing some, each standard test weight quality is different, by the standard test weight random loading of different quality on truck scale body,
nroad sensor just has
nindividual output data,
nindividual output data and corresponding standard test weight quality form one group of training sample and are kept in outer computer;
II) construct training objective function, its relational expression is formula (4):
（4）；
III) ask the derivative of the desirable weighting model of truck scale, its relational expression is formula (5):
（5）；
IV) ask the weight coefficient of desirable weighting model in training objective function
μ j , its relational expression is formula (6):
（6）；
V) ask third layer output layer
derivative, its relational expression is formula (7):
（7）；
VI) ask respectively
w, b (1) , Vwith
b (2)increment Delta
v m , Δ
b (2), Δ
w mi , Δ
b m (1), and right
w, b (1) , Vwith
b (2)upgrade, their relational expression is respectively formula (8), (9):
（8）
（9）
In formula (9),
v new m ,
b (2)
new ,
w new mi ,
b m (1)
new be respectively
v m ,
b (2),
w mi ,
b m (1)value after renewal,
v old m ,
b (2)
old ,
w old mi ,
b m (1)
old be respectively
v m ,
b (2),
w mi ,
b m (1)value before renewal;
Vii) arrange training starting condition, carry out setting training according to formula (7) ~ (9), the error amount that training is produced, in setting range, obtains the weight matrix of input layer to hidden layer respectively
w, hidden layer bias vector
b (1), hidden layer is to the weight vector of output layer
v, output layer bias
b (2)end value, and be kept in the storage element of microprocessor, for called during truck scale online weighing;
In the training process to desirable weighting model, first utilize the training sample gathered in actual weighting model training process, then utilize least square method to train, obtain final coefficient
p i , its relational expression is formula (10):
（10）
In formula (10),
pfor
p i vector, namely
p=[
p 1,
p 2...,
p n ]
t,
nfor the number of LOAD CELLS;
x=[
x 1,
x 2...,
x j...,
x k ] be input amendment matrix,
y=[
y 1,
y 2,
y j ...,
y k ] be output sample matrix.
2. the Weighing method being applicable to truck scale according to claim 1, is characterized in that: described online weighing should carry out after actual weighting model training is qualified, and its step is as follows:
1) will collect
nthe weighing-up wave of road LOAD CELLS is as the input vector of BP neural network ground floor
x;
2) by input vector
xwith the actual weighting model parameter of preserving in the memory unit
w,
b (1),
vand b
(2)substitute into together in formula (3), try to achieve BP neural network and export
for final weighing results;
3) display shows final weighing results.
3. the Weighing method being applicable to truck scale according to claim 1 and 2, it is characterized in that: the method that described weighing-up wave gathers is as follows, the data obtained after the output signal of each road LOAD CELLS is carried out signal amplification, filtering and analog-to-digital conversion process, train the input vector with online weighing as desirable weighting model and actual weighting model
x.
4. the Weighing method being applicable to truck scale according to claim 1 and 2, is characterized in that: described microprocessor is single-chip microcomputer, dsp processor or other embedded system devices, and with storage unit.
5. the Weighing method being applicable to truck scale according to claim 3, is characterized in that: described microprocessor is single-chip microcomputer, dsp processor or other embedded system devices, and with storage unit.
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