CN110705645A - English teaching quality assessment method based on SOFM neural network - Google Patents
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Abstract
The invention relates to an English teaching quality assessment method based on a SOFM neural network, which solves the technical problem of complex output results and constructs a SOFM neural network model formed by connecting two stages of SOFM neural sub-networks in series by adopting step 1; step 2, defining a threshold value R, inputting English teaching quality evaluation scores, carrying out SOFM learning algorithm training on a 1 st-level SOFM neural sub-network, and calculating various initial clustering centers; step 3, judging whether the neuron in the competition layer is deleted or not; step 4, judging whether the neurons in the competition layer are clustered secondarily; and step 5, dividing English teaching object data to be evaluated into c evaluation grades according to the final clustering center value and outputting the evaluation grades, so that the problem is well solved, and the method can be used for English teaching.
Description
Technical Field
The invention relates to the field of English teaching, in particular to an English teaching quality assessment method based on a SOFM neural network.
Background
English teaching refers to the process of teaching english to persons whose english language is or is not the first language. English teaching relates to many professional theoretical knowledge, including linguistics, second language acquisition, glossaries, sentence syntactics, literature, corpus theory, cognitive psychology, etc. English teaching is a progressive process, and English learning is crucial today in globalization and rapid development, whether for people who have English in the first language or not.
Most of the existing English teaching quality assessment is embodied by scoring, is complex and is not visual enough. The invention provides an English teaching quality evaluation method based on a SOFM neural network, which is used for further optimizing the learning performance of the neural network on the basis of solving the problems.
Disclosure of Invention
The invention aims to solve the technical problem of complex output result in the prior art. The English teaching quality assessment method based on the SOFM neural network has the characteristic of visual results.
In order to solve the technical problems, the technical scheme is as follows:
a method for evaluating English teaching quality based on a SOFM neural network comprises
Step 1, constructing a SOFM neural network model connected in series by two stages of SOFM neural sub-networks, wherein the 1 st stage of SOFM neural sub-network is composed of gamma 1 SOFM neural network units, the 2 nd stage of SOFM neural sub-network is composed of gamma 2 SOFM neural network units connected in parallel, and gamma 1 is less than gamma 2;
step 2, defining the number c of English teaching quality evaluation grades, defining a threshold value R for evaluating neuron merging or splitting, inputting English teaching quality evaluation scores, carrying out SOFM learning algorithm training on a 1 st-level SOFM neural subnetwork, and calculating various initial clustering centers;
step 3, judging whether 1 neuron corresponding sample of the competition layer neurons is lower than a sample number threshold value, and if so, deleting the corresponding competition layer neurons;
step 4, judging whether the competitive layer neurons have 1 neuron corresponding to more than 2 rating levels, if so, calling a 2 nd-level SOFM neural sub-network to perform SOFM learning algorithm training, and then outputting various clustering center values as final clustering center values, otherwise, taking the various initial clustering center values in the step 2 as final clustering center values wij；
And 5, dividing English teaching object data to be evaluated into c evaluation grades according to the final clustering center value and outputting the evaluation grades.
In the above scheme, for optimization, step 2 further comprises
Step (1) of calculating the inter-class distance D of each classj＝||mj-mj+1||，j＝1，2，3，......c-1；
Step (2) comparing the inter-class distances DjAnd a threshold value R, if Dj<R, the number of corresponding samples of the neuron j is lower than the threshold value of the number of samples, wherein mjIs the cluster center value of the jth class.
Further, step 3 comprises:
step (3), calculating the average distance in each class
Step (4), comparing the average distance djAnd a threshold value R, if dj>R, then neuron j corresponds to more than 2 rating levels, xiFor the English teaching quality evaluation score value, n, corresponding to the input layer neuron ijAre scores within a class.
Further, step 4 further includes correcting the final cluster center value, where the correction amount is:
Δmij＝mij(t+1)-mij(t)＝β(t)(xi(t)-mij(t))；
wherein m isij(t +1) is the weight between the input layer i neuron and the mapping layer j neuron at the moment of t +1, mij(t) is the weight between the input layer i neuron and the mapping layer j neuron at the time t;
the learning rate of the neural network at the time t, and t is the neural network time.
Further, when the English teaching quality evaluation score is input in the step 2, the English teaching quality evaluation score is preprocessed to be a rough sampling, and normalization processing is performed after preprocessing.
The invention has the beneficial effects that: according to the invention, by constructing the double-layer SOFM network model, the scale of the SOFM is adaptively adjusted according to the number of input samples and the number of output levels, and the classification accuracy can be ensured. Meanwhile, after the weights of the winning neuron and all the neurons in the neighborhoods of the winning neuron are corrected, the learning rate is adjusted, the size of the neighborhood range of the neuron which needs to be adjusted along with the input layer is gradually changed, and the classification accuracy is improved.
Drawings
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a schematic diagram of a method for evaluating the quality of english teaching in example 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The embodiment provides an english teaching quality assessment method based on SOFM neural network, as shown in fig. 1, including:
step 1, constructing a SOFM neural network model connected in series by two stages of SOFM neural sub-networks, wherein the 1 st stage of SOFM neural sub-network is composed of 1 SOFM neural network unit, and the 2 nd stage of SOFM neural sub-network is composed of 2 SOFM neural network units connected in parallel; the number of the SOFM neural network units can be adjusted, and the principle is that the number of the first layer is far smaller than that of the second layer;
step 2, defining the number c of English teaching quality evaluation grades, defining a threshold value R for evaluating neuron merging or splitting, inputting English teaching quality evaluation scores, carrying out SOFM learning algorithm training on a 1 st-level SOFM neural subnetwork, and calculating various initial clustering centers;
step 3, judging whether 1 neuron corresponding sample of the competition layer neurons is lower than a sample number threshold value, and if so, deleting the corresponding competition layer neurons;
step 4, judging whether the competitive layer neurons have 1 neuron corresponding to more than 2 rating levels, if so, calling a 2 nd-level SOFM neural sub-network to perform SOFM learning algorithm training, and then outputting various clustering center values as final clustering center values, otherwise, taking the various initial clustering center values in the step 2 as final clustering center values wij；
And 5, dividing English teaching object data to be evaluated into c evaluation grades according to the final clustering center value and outputting the evaluation grades.
The SOFM learning algorithm in this embodiment is completed using an existing training method.
Specifically, step 2 comprises
Step (1) of calculating the inter-class distance D of each classj＝||mj-mj+1||，j＝1，2，3，……c-1；
Step (2) comparing the inter-class distances DjAnd a threshold value R, if Dj<R, the number of corresponding samples of the neuron j is lower than the threshold value of the number of samples, wherein mjIs the cluster center value of the jth class.
Specifically, step 3 includes:
step (3), calculating the average distance in each class
Step (4), comparing the average distance djAnd a threshold value R, if dj>R, then neuron j corresponds to more than 2 rating levels, xiFor the English teaching quality evaluation score value, n, corresponding to the input layer neuron ijAre scores within a class.
Preferably, step 4 further includes correcting the final cluster center value by:
Δmij＝mij(t+1)-mij(t)＝β(t)(xi(t)-mij(t))；
wherein m isij(t +1) is the weight between the input layer i neuron and the mapping layer j neuron at the moment of t +1, mij(t) is the weight between the input layer i neuron and the mapping layer j neuron at the time t;
the learning rate of the neural network at the time t, and t is the neural network time.
Preferably, when the English teaching quality evaluation score is input in the step 2, the English teaching quality evaluation score is preprocessed to be a rough sample, and normalization processing is performed after preprocessing. The score is subjected to rough processing and normalization processing, so that the learning consumption can be reduced.
Although the illustrative embodiments of the present invention have been described above to enable those skilled in the art to understand the present invention, the present invention is not limited to the scope of the embodiments, and it is apparent to those skilled in the art that all the inventive concepts using the present invention are protected as long as they can be changed within the spirit and scope of the present invention as defined and defined by the appended claims.
Claims (5)
1. An English teaching quality assessment method based on a SOFM neural network is characterized in that: the English teaching quality assessment method based on the SOFM neural network comprises the following steps:
step 1, constructing a SOFM neural network model connected in series by two stages of SOFM neural sub-networks, wherein the 1 st stage of SOFM neural sub-network is composed of gamma 1 SOFM neural network units, the 2 nd stage of SOFM neural sub-network is composed of gamma 2 SOFM neural network units connected in parallel, and gamma 1 is less than gamma 2;
step 2, defining the number c of English teaching quality evaluation grades, defining a threshold value R for evaluating neuron merging or splitting, inputting English teaching quality evaluation scores, carrying out SOFM learning algorithm training on a 1 st-level SOFM neural subnetwork, and calculating various initial clustering centers;
step 3, judging whether 1 neuron corresponding sample of the competition layer neurons is lower than a sample number threshold value, and if so, deleting the corresponding competition layer neurons;
step 4, judging whether the competitive layer neurons have 1 neuron corresponding to more than 2 rating levels, if so, calling a 2 nd-level SOFM neural sub-network to perform SOFM learning algorithm training, and then outputting various clustering center values as final clustering center values, otherwise, taking the various initial clustering center values in the step 2 as final clustering center values wij；
And 5, dividing English teaching object data to be evaluated into c evaluation grades according to the final clustering center value and outputting the evaluation grades.
2. The SOFM neural network-based english teaching quality assessment method of claim 1, characterized in that: step 2 comprises
Step (1) of calculating the inter-class distance D of each classj＝||mj-mj+1||，j＝1，2，3，......c-1；
Step (2) comparing the inter-class distances DjAnd a threshold value R, if Dj<R, the number of corresponding samples of the neuron i is lower than the sample number threshold value, wherein mjIs the cluster center value of the jth class.
3. The SOFM neural network-based english teaching quality assessment method of claim 2, characterized in that: the step 3 comprises the following steps:
step (3), calculating the average distance in each class
Step (4), comparing the average distance djAnd a threshold value R, if dj>R, then neuron j corresponds to more than 2 rating levels, xiFor the English teaching quality evaluation score value, n, corresponding to the input layer neuron ijAre scores within a class.
4. The SOFM neural network-based english teaching quality assessment method of claim 1, characterized in that: step 4, correcting the final clustering center value, wherein the correction is as follows:
Δmij＝mij(t+1)-mij(t)＝β(t)(xi(t)-mij(t))；
wherein m isij(t +1) is the weight between the input layer i neuron and the mapping layer j neuron at the moment of t +1, mij(t) is the weight between the input layer i neuron and the mapping layer j neuron at the time t;
the learning rate of the neural network at the time t, and t is the neural network time.
5. The SOFM neural network-based english teaching quality assessment method of claim 1, characterized in that: and 2, when the English teaching quality evaluation score is input, preprocessing the English teaching quality evaluation score, wherein the preprocessing is rough sampling, and normalization processing is performed after preprocessing.
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| US20030140019A1 (en) * | 2002-01-22 | 2003-07-24 | Koninklijke Philips Electronics N.V. | Self-organizing feature map with improved performance by non-monotonic variation of the learning rate |
| CN108416429A (en) * | 2018-02-24 | 2018-08-17 | 华北水利水电大学 | Hydrogen-fuel engine fault diagnosis system and its method based on SOM neural networks |
| CN108898154A (en) * | 2018-09-29 | 2018-11-27 | 华北电力大学 | A kind of electric load SOM-FCM Hierarchical clustering methods |
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