# The Construction of the Development Mode of School-Enterprise Cooperation in Higher Vocational Education with the Aid of Sensitive Neural Network

Authors: Xiangbin Hu; Aidi Tan; Yan Gao
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

In-depth school-enterprise cooperation can promote the transformation of higher vocational colleges’ school-running concept and talent training mode, deepen the teaching reform of higher vocational colleges, and improve the employment rate of higher vocational colleges. In the context of school-enterprise cooperation, general higher vocational schools face huge challenges and opportunities in employment guidance. Under the school-enterprise cooperation model, the internship management of students during the internship process is a joint management behavior between the school and the enterprise, which is of great significance to deepen the school-enterprise cooperation. This paper changes the traditional analysis mode of school-enterprise cooperation in higher vocational education and applies sensitive neural network to the construction of school-enterprise cooperation development model in higher vocational education. On the basis of the sensitivity of Adaline neurons, this paper further gives the definition of the sensitivity of the Madaline network structure, deduces the sensitivity calculation of the Madaline network structure without too many constraints, and constructs an intelligent model. Through experimental research, we know that the school-enterprise cooperative development model of higher vocational education based on sensitive neural network proposed in this paper has good results.


---

## Body

## 1. Introduction

The school-enterprise cooperation model is a guarantee platform for in-depth and sustainable development cooperation between schools and enterprises. The establishment of a school-enterprise cooperation model is based on theories of resource scarcity, constructivism, and humanism and provides a reference for the domestic vocational education community. Domestic colleges and universities have also tried four types of cooperation based on the successful experience of foreign countries, such as order-based and joint-stock management [1]. Both parties of school-enterprise cooperation have their own interests and needs. As a production organization, the fundamental purpose of an enterprise is to maximize profits and increase economic benefits. The school’s mission is to cultivate talents and pursue the social benefits of educational achievements and talent cultivation. There is a big deviation in the pursuit of cooperation goals and cooperation interests between the two parties, and it is difficult to find the combination of interests of both parties, which makes it difficult for school-enterprise cooperation to deepen [2]. Therefore, building a win-win platform for school-enterprise cooperation is an inevitable choice to resolve this contradiction, and the choice of this platform is the choice of cooperation mode. The research on the school-enterprise cooperation model is to focus on how to establish a deep and continuous close relationship between schools and enterprises and ultimately achieve a win-win situation. Under the guidance of the “employment-oriented” education policy, vocational colleges have deeply understood the necessity and importance of school-enterprise cooperation and must actively seek partners [3].What issues should be paid attention to in the strategic choice of the driving force and mode of school-enterprise cooperation, and whether there are special concerns of its own, how to ensure the continuous and efficient operation of vocational colleges’ school-enterprise cooperation in the full market competition, etc. The issue is under intense discussion.Neural network is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural network and performs distributed parallel information processing. This kind of network depends on the complexity of the system and achieves the purpose of processing information by adjusting the interconnected relationship between a large number of internal nodes. Neural networks can be used for pattern recognition, signal processing, knowledge engineering, expert systems, optimal composition, robot control, etc. With the continuous development of neural network theory itself and related theories and related technologies, the application of neural network will be more in-depth.Schools and society have raised the employment rate to a very high level. After the new century, such issues have been frequently mentioned, and employment issues have become the focus of social attention. In recent years, despite the strong support from all walks of life and the promulgation of relevant laws, the pressure for enrollment expansion has been unstoppable. During the implementation of the policy, the number of fresh graduates each year is larger than that of the previous year, and they need employment guidance to take them out of the predicament. Due to the continuous expansion of the scope of higher education in our country, it is a universal appeal to help graduates to effectively face market competition, increase their success rate, offer guidance courses, and improve guidance, which has also attracted widespread attention. The development of school-enterprise cooperation in our country is not yet mature enough, and there is indeed a big gap between our country and other countries in terms. Employment is very beneficial. There are mainly two aspects. From a theoretical point of view, on the one hand, career guidance is related to the success rate of students’ employment, and on the other hand, it affects the school’s social image and reputation. There are huge shortcomings in this work, and the relevant conclusions are in a blank state. This is the primary reason for restricting the orderly progress of this work.


## 2. Related Work

School-enterprise cooperation is the basic school-running model of vocational education and the key to running vocational education well. In order to thoroughly implement the spirit of the 19th National Congress of the Communist Party of China, implement the requirements of the “Decision of the State Council on Accelerating the Development of Modern Vocational Education,” improve the vocational education and training system, and deepen the integration of production and education and school-enterprise cooperation, the Ministry of Education, together with the National Development and Reform Commission, the Industry The Ministry of Information Technology, the Ministry of Finance, the Ministry of Human Resources and Social Security, and the State Administration of Taxation have formulated the Measures for the Promotion of Vocational School-Enterprise Cooperation. The literature [4] believed that advancing the order-based talent cultivation work and improving the relevant curriculum system can alleviate the employment pressure and the tension of excessive increase in the number of people in each session. The literature [5] analyzed the employment competitiveness of graduates from the internal and external environment and believed that it is necessary to establish a precise talent cultivation system and pay attention to the construction of students’ thinking. The literature [6] proposed that schools should establish communication with relevant departments from the perspective of the labor force and education service market and at the same time improve the employment access mechanism. The literature [7] described the growth process of higher education, analyzes the form of employment development, and clarifies the important role of internal factors from the school itself, teachers, and students. The literature [8] analyzed the advantages, disadvantages, opportunities, and threats of graduates’ employment. Moreover, from the level of government, employers, schools, and students, it is proposed to integrate the advantages of employment into the characteristics of higher education, and use the advantages to alleviate the tense employment situation of graduates. The literature [9] has taken a full range of investigations on the employment situation in terms of guidance, majors, location intentions, degree of satisfaction, approaches, disadvantages, and orientations. Moreover, it analyzed the results of the survey and pointed out that schools need to strengthen entrepreneurship education, highlight the characteristics of running schools, and the government should improve policies and regulations. The literature [10] found that higher vocational education has conflicts with many aspects, and it clarified that schools should learn to accurately position and cultivate high-tech talents. The literature [11] mentioned that the current employment situation is severe, and it is necessary to build characteristic disciplines and attach importance to the construction of teaching staff.Literature [12] analyzes the employment situation in my country from the advantages and disadvantages of employment and the problems and opportunities faced and points out that the external environment plays a decisive role. The literature [13] believes that the high employment rate in today’s society is not a good thing. The employment positions are inconsistent with the expectations of graduates. The positions they obtain are not exclusive and cannot enter the main labor market. Literature [14] discusses that current graduates have poor mentality and self-awareness. In order to break this dilemma, they need to cultivate their correct career outlook, adopt psychological counseling methods for them, and take relevant guidance at each stage of their studies. Literature [15] pointed out that such problems originated from society, employers, schools, and students and proposed that in order to get rid of this status quo, it is necessary to strengthen government protection and improve its own system. Literature [16] objectively analyzes the reasons for the dislocation of current students’ employment and proposes solutions from two aspects: school-enterprise cooperation and deepening education reform.Literature [17] believes that school-enterprise cooperation should pay attention to quality education and employment guidance, and its success requires time accumulation and good communication with enterprises. Literature [18] believes that the school-enterprise cooperation project in Fujian Province can develop continuously only if the government, universities, and enterprises develop in a balanced way and communicate with each other. In the study of mechanism, the literature [19] pointed out that factors are divided into external and internal factors. If mutual assistance is to be sustained, it is the key to establish a drive that conforms to the interests of both parties. Literature [20] believes that in the past two decades, especially in the last ten years of reform and opening up, when enterprises reflect on their own growth and future prospects, they have realized the great significance of education and the development of talents to the growth of enterprises. To this end, we must promote various horizontal contacts between enterprises and schools. The so-called two-way participation between us and many universities not only provides a kind of intelligence to ensure the sustainable development of personnel and enterprises but also promotes the reform of university education. The purpose of this paper is to study the role of sensitive neural network in the construction of higher vocational school-enterprise cooperation development model and also propose the basic algorithm of neural network.


## 3. Application of Madaline Network Structure Sensitivity in School-Enterprise Cooperation in Higher Vocational Education

Sensitivity is to study the dependence of the network output on the changes of its parameters (weights, inputs, structure, etc.). Existing research results mainly focus on the sensitivity calculations under the changes of weight parameters and input parameters. The increase or decrease of nodes in the network will cause the output of the network to change. This article will use this as a basis to further explore the sensitivity calculation problems under structural changes.In order to find a suitable network structure, the commonly used method is to cut the trained large-structure network into a small-structure network, while retaining some properties that it must have. The sensitivity of Adaline neurons can hit the mother, and Liang Zhijian complements each other. The network is composed of layers. Under parameter changes, the network layer sensitivity and network s, the input and weight after disturbance areX^∈−1,1n and W^∈Rn0⊲i≤nl,1≤l≤L, respectively. The layer sensitivity of the Madaline network is defined as an n-dimensional vector composed of the sensitivity of all output Adaline neurons in the lth layer, denoted as:
(1)Sl=s1l,s2l,⋯,snll.Sensitivity is an important feature that characterizes the response of neurons to external stimuli. The sensitivity of neurons to input is conducive to the acquisition and encoding of signals. Acupuncture and electrode stimulation are inputs with fluctuations, and the sensitivity of neurons to it is studied, which help to understand its mechanism of action. The sensitivity of the Madaline network is the sensitivity of the output layer of the network, namely [21]
(2)Snet=SL=s1l,s2l,⋯,snll.Among them,sil1≤i≤nl represents the sensitivity of the ith Adaline neuron in the output of the first layer of the Madaline network.Changes in the network structure are concentrated in the increase (decrease) of the number of hidden nodes (neurons) in the network. Then, what is the change in the output of the network caused by the increase or decrease of the hidden layer nodes of the network? The definition of the sensitivity of the Madaline network structure will be given below.For a Madaline network with structuren0−n1−n2⋯nL, we assume that the hidden layer node to be deleted is the ith (1≤i≤L) layer j (1≤j≤n) neuron, denoted as nodeji. All input samples are denoted as snetnodeji.According to formula (2), snetnodeji can be expressed as
(3)snet−nodeji=SL=s1l,s2l,⋯,snllT.In formula (3), the network structure sensitivity is just a vector form. For the convenience of measurement, it can be further quantified as
(4)snet−nodeji=∑i=1nLsiL⋅VnnLVn=1nL∑i=1nLsiL.Among them,siL1≤i≤nL represents the sensitivity of the ith Adaline neuron.According to formula (4), the sensitivity of the Madaline network structure is the average value of the sensitivity of all Adaline neurons in the output layer of the network due to changes in the network structure.In the learning process of a neural network, the number of neurons in the input layer and output layer of the network is specifically determined by the learning task. However, the determination of the number of hidden layer nodes requires continuous learning and exploration. Because this is a complex issue, there is no theoretical guidance yet. Due to research needs, a sufficient number of hidden layer neuron nodes can realize the relevant mapping relationship, so this paper uses the single hidden layer Madaline network as a model to calculate the network structure sensitivity.The smallest and most important unit in an artificial neural network is the neuron. Like the biological nervous system, these neurons are connected to each other, and they have great processing power. In general, artificial neural networks try to replicate the behavior and processes of real brains. We assume that thej1≤j≤n1th node in the hidden layer of the Madaline network is deleted. The direct consequence of the deletion of this node is that the jth input component xk,j21≤k≤n2 is missing from any node k1≤k≤n2 in the output layer of the network, which in turn makes its input weighted sum Wk2TXk2 change from the original ∑i=0n1wk,i2xk,i2 to ∑i=0i≠jn1wk,i2xk,i2.
(5)∑i=0n1wk,i2xk,i2⟶∑i=0i≠jn1wk,i2xk,i2.Due to(6)∑i=0i≠jn1wk,i2xk,i2=∑i=0i≠jn1wk,i2xk,i2+wk,j2−wk,j2xk,j2.Among them,1≤j≤n1,1≤k≤n2.It can be seen from formula (6) that the effect of reducing any output node k1≤k≤n2 given by the jth hidden layer node can be equivalently transformed into a disturbance of the jth weight wk,j2 of the output node with an equivalent value of −wk,j2, that is, wk,j2=−wk,j2.For the sake of intuition, this equivalent conversion can be further shown in Figure1. Figure 1(a) vividly describes the actual conduction process of the clipping operation in the network structure, that is, the hidden layer reduces one node, and the subsequent layer nodes will reduce the corresponding one-dimensional input component. Figure 1(b) graphically depicts the equivalent effect of structural tailoring. That is, the real impact of the deletion of a hidden layer node on the output layer node is equivalent to a weight component disturbance Δwk,j2=−Δwk,j2 on the corresponding input component of each node in the output layer when the hidden layer node is not deleted.Figure 1
The effect of hidden layer node reduction on output layer nodes.Therefore, in the case of deleting thejth hidden layer node, the sensitivity calculation problem of any output layer node k1≤k≤n2 can be transformed into the sensitivity calculation problem of equivalent weight parameter disturbance, namely,
(7)sk2−nodej1=sk2ΔWk,i2,Δwk,i2=0i≠k−wk,i2i=j.Among them,Δwk,i20≤i≤n1 is the ith weight change component of Δwk2.Similarly, if a node is added to the hidden layer of the network, then the input layer of the corresponding network should add one dimension (the input becomes (n+1) dimensional), that is, any node k1≤k≤n2 in the output layer adds one dimensional (n1+1th dimension) input component. The effect is equivalent to the existence of the n+1th dimension of the input vector, but the corresponding weight parameter component is 0, as shown in Figure 2.Figure 2
The effect of the increase of hidden layer nodes on the output layer nodes.Therefore, when thejth hidden layer node is added, the sensitivity calculation problem of any output layer node k1≤k≤n2 can be transformed into the sensitivity calculation problem of equivalent weight parameter disturbance, namely,
(8)sk2+nodej1=sk2ΔWk,i2,Δwk,i2=0i≠n1+1wk,i2i=n1+1.Among them,Δwk,i20≤i≤n1 is the ith weight change component of Δwk2, and Δwk,n1+12 is the corresponding weight component assigned to the newly added node ηt2=∑i=1n1wk,i2xk,i2.For all Adaline neurons in the first layer of the network, their input is the same, that is,X1=X, which is also the input of the Madaline network. For all Adaline neurons in the last layer, their output constitutes the output of the entire Madaline network. According to formula (4), in order to obtain the sensitivity of the Madaline network structure, we must first obtain the sensitivity of all Adaline neurons in the last layer of the network due to changes in the network structure. The detailed derivation process of Madaline network structure sensitivity calculation is given below.We set the random variable to beξt2=∑i=1n1wi,k2xk2, and the random variable to be ηt2=∑i=1n1w^i,k2xk2, where 1≤i≤n1.Among them,wi,k2 represents the kth component of the weight corresponding to the input of the ith Adaline neuron in the second layer, and xk2 represents the kth component of the input of all Adaline neurons in the second layer. ξt2 is the weighted sum of the ith Adaline neuron in the second layer, and w^i,k2 represents the kth component after the weight corresponding to the input of the ith Adaline neuron in the second layer is disturbed, and ηt2 is the weighted sum of the weight parameters of the ith Adaline neuron in the second layer after being disturbed.The sensitivity calculation process of the Adaline neuron has been deduced in detail in the previous section. Therefore, whenn is large enough, the random variables ξi2 and ηi2 can be regarded as obeying a two-dimensional normal distribution, namely,
(9)ξi2,ηi2~Nμi2,μ^i2,σi22,σ^i22,ρξi2,ηi2.Among them,σi22 is the mathematical expectation of the random variable ξi2, and μ^i2 is the mathematical expectation after the weight parameter is disturbed (that is, the random variable ηi2). σi22 is the variance of the random variable ξi2, σ^i22 is the variance after the weight parameter is disturbed (that is, the random variable ηi2), and ρξi2,ηi2 represents the correlation coefficient between the random variables ξi2 and ηi2.The following first gives the expected solution process of random variablesξi2 and ηi2, denoted as σi22 and μ^i2, respectively, as follows:
(10)μi2=Eξi2=E∑i=1n1wi,k2xk2=∑i=1n1wi,k2Exk2,1≤k≤n1,(11)μi2=Eηi2=E∑i=1n1w^i,k2xk2=∑i=1n1w^i,k2Exk2,1≤k≤n1.TheExk2 in formula (9) is the mathematical expectation of the kth component xk2 of the second layer input. The mathematical expectation μk⟶2 of xk2 and the solution process of the variance σ2k⟶2 are given below:
(12)μk⟶2=Exk2=1−qk2−qk2=1−2qk2,(13)σ2k⟶2=Dxk2=Exk2−Exk22=4qk21−qk2.Among them,1≤k≤n1 and qk2 represent the distribution of the kth component xk2 value of the input of the layer -1. Therefore, formulas (9) and (10) can be further written as
(14)μi2=∑i=1n1wi,kLμk⟶2,1≤k≤n1,(15)μ^i2=∑i=1n1w^i,kLμk⟶2,1≤k≤n1.Similarly, the variances of random variablesξi2 and ηi2 are denoted as σi22 and σ^i22, respectively, and the detailed derivation is as follows
(16)σi22=Dξi2=D∑i=1n1wi,k2xk2=∑k0=1n1∑k1=1n1wi,k2wi,k2Exk02−Exk02xk12−Exk12=∑k0=1n1∑k1=1n1wi,k2wi,k2Exk02−μk0⟶2xk12−Eμk1⟶2=∑k0=1n1∑k1=1n1wi,k2wi,k2Exk02xk12−μk0⟶2μk1⟶2.Among them, onlyExk02xk12 is unknown, and the specific solution process is as follows:According to the knowledge related to probability and statistics, there are(17)ρλh02λh12=covxk02xk12Dxk02Dxk12=Exk02xk12−μk0⟶2μk1⟶2σk0⟶2σk1⟶2.Therefore, there are(18)Exk02xk12=ρxk02xk12σk0⟶2σk1⟶2+μk0⟶2μk1⟶2.Hereρλh02λh12 is the correlation coefficient of random variables xk02 and xk12.Regarding the random variablesxk02 and xk12, there is the following relationship:
(19)xk02=−1,ξk01=∑k=1nwh0,k1xk1<0,+1,ξk01=∑k=1nwk0,k1xk1≥0,(20)xk12=−1,ξk11=∑k=1nwk1,k1xk1<0,+1,ξk11=∑k=1nwk1,k1xk1≥0.Therefore, we make an approximate derivation:(21)ρxk02xk12=ρξk02ξk12.Substituting formula (20) into formula (17) and formula (16), formula (15) can be further written as
(22)σi22=Dξt2=∑k0=1n1∑k1=1n1wi,k2wi,k2ρξk02ξk12σ⟶k02σ⟶k12,1≤k0,k1≤n1.Similarly,σ^i22 can be written as
(23)σ^i22=Dηi2==∑k0=1n1∑k1=1n1w^i,k2w^i,k2ρηk02ηk12σ^⟶k02σ^⟶k12,1≤k0,k1≤n1.Among them,ρηk02ηk12 represents the correlation coefficient between k0 and k1 and between any two nodes in the first layer after the network structure changes.According to the knowledge of probability and statistics, the correlation coefficient of random variablesξi2 and ηi2 can be written as the following formula:
(24)ρλh02λh12=covξi2ηi2Dξi2Dηi2=∑k0=1n1∑k1=1n1wi,k2wi,k2ρξk02ξk12σ⟶k02σ⟶k12σk02σ^k12,1≤k0,k1≤n1.The parameterP contains the unknown parameter qP, and σ^i22 and ρξk02ξη12 contain the correlation coefficients ξi2, ρηk02ηk12, and ρξk02ξη12 of the previous layer, respectively. The solution process of these unknown parameters will be given in detail below:
(25)qk2=Pxk2=−1=Pξk1<0=Pξk1−μk1σk1<−μk1σk1=Φ−μk1σk1,1≤k≤n1,(26)covξk01,ξk11=cov∑k0=1n0wi,k01xk01,∑k1=1n0wj,k11xk11=∑k0=1n0∑k1=1n0wj,k11wi,k01covxk11xk01=∑k=1n0wi,k01wj,k11Dxk1=∑k=1n0wi,k01wj,k11σ2k⟶1.Here, since there iscovxk11xk01=0 when k0≠k1, there are
(27)ρξi2ξj2=covξi1,ξj1/Dξi1Dξj1=∑k=1n0wi,k01wj,k11σ2k⟶1/σi1σj1.Similarly, there are(28)ρηi2ηj2=covηi1,ηj1/Dηi1Dηj1=∑k=1n0w^i,k01w^j,k11σ2k⟶1/σ^i1σ^j1,(29)ρξi2ηj2=covξi1,ηj1/Dξi1Dηj1=∑k=1n0xk,x^kwi,k01w^j,k11σ∧2k⟶1/σi1σ^j1.Among them,σ2k⟶1 represents the variance corresponding to the weighted sum of the network input X of the kth node when there is no structural change, that is, D∑k=1nxk. σ∧2k⟶1 represents the variance corresponding to the weighted sum of the network input X of the kth node when the structure changes, that is, D∑k=1nxk. σi1 represents the variance of the random variable ξi1 corresponding to the ith node in the first layer when there is no structural change, and σi1 represents the variance of the random variable ηi1 corresponding to the ith node in the first layer when the network structure changes.At this point, all the parameters in formula (26) have been obtained, and the joint density function m of the random variable d and the random variable fξ,η is easily obtained. Finally, similar to the method of solving Adaline neuron sensitivity in the previous section, the sensitivities of all neurons in the last layer of the Madaline network are obtained. According to formula (24), the structural sensitivity result of the network due to structural disturbance can be obtained.In order to verify the correctness of the above-mentioned network structure sensitivity calculation, the experiment in the following sections will mainly compare the network sensitivity caused by the decrease and increase of hidden layer nodes with the real sensitivity.


### 3.1. Reduce Hidden Nodes

In this part of the experiment, the hidden layer nodes will be randomly cropped. Five groups of experiments will, respectively, crop 1-5 nodes in the hidden layer. Each experiment will be performed 30 times. The initial value of the weight of each experiment is a random number of [-1, 1]. The network input in the experiment is parity check data, the specific sample number is shown in Table1, the experimental network structure is shown in Table 2, and the experimental results are shown in Figure 3.Table 1
Experimental data set.
Network input dimensionNumber of samples515108002015003032000401000000Table 2
Experimental network structure.
Network input dimensionNetwork structureNumber of hidden layer nodesNetwork structure after node trimming55-5-115-4-11010-10-1210-8-12020-15-1320-12-13030-20-1430-16-14040-25-1540-20-1Figure 3
Comparison of the sensitivity of clipping hidden layer nodes.
(a)
5-4-1(b)
10-16-1(c)
20-12-1(d)
30-16-1(e)
40-20-1It can be seen from Figure3 that the structural sensitivity of the hidden layer clipping node simulated by the algorithm in this paper is consistent with the real sensitivity trend. When the input dimension of the network gradually increases, this trend becomes more obvious, and the result of the simulation experiment is closer to the real sensitivity, which reflects the effectiveness and practicality of the algorithm in this paper.


### 3.2. Add Hidden Nodes

This part of the experiment will add nodes to the hidden layer of the network. 5 groups of experiments are added 1-5 nodes in the hidden layer, each group of experiments is carried out 30 times, and the initial value of the weight of each experiment is a random number of [-1, 1]. The experimental data set is shown in Figure1, the experimental network structure is shown in Table 3, and the experimental results are shown in Figure 4.Table 3
Experimental network structure.
Network input dimensionNetwork structureNumber of hidden layer nodesNetwork structure after node trimming55-5-155-10-11010-10-1410-14-12020-15-1320-18-13030-20-1230-22-14040-25-1140-26-1Figure 4
Sensitivity comparison of adding hidden layer nodes.
(a)
5-4-1(b)
10-14-1(c)
20-18-1(d)
30-22-1(e)
40-26-1It can be seen from Figure4 that the structural sensitivity simulated by the algorithm in this paper is consistent with the real sensitivity trend due to the increase of the hidden layer. When the input dimension of the network gradually increases, this trend becomes more obvious, and the result of the simulation experiment is closer to the real sensitivity, which reflects the effectiveness and practicality of the algorithm in this paper.


## 3.1. Reduce Hidden Nodes

In this part of the experiment, the hidden layer nodes will be randomly cropped. Five groups of experiments will, respectively, crop 1-5 nodes in the hidden layer. Each experiment will be performed 30 times. The initial value of the weight of each experiment is a random number of [-1, 1]. The network input in the experiment is parity check data, the specific sample number is shown in Table1, the experimental network structure is shown in Table 2, and the experimental results are shown in Figure 3.Table 1
Experimental data set.
Network input dimensionNumber of samples515108002015003032000401000000Table 2
Experimental network structure.
Network input dimensionNetwork structureNumber of hidden layer nodesNetwork structure after node trimming55-5-115-4-11010-10-1210-8-12020-15-1320-12-13030-20-1430-16-14040-25-1540-20-1Figure 3
Comparison of the sensitivity of clipping hidden layer nodes.
(a)
5-4-1(b)
10-16-1(c)
20-12-1(d)
30-16-1(e)
40-20-1It can be seen from Figure3 that the structural sensitivity of the hidden layer clipping node simulated by the algorithm in this paper is consistent with the real sensitivity trend. When the input dimension of the network gradually increases, this trend becomes more obvious, and the result of the simulation experiment is closer to the real sensitivity, which reflects the effectiveness and practicality of the algorithm in this paper.


## 3.2. Add Hidden Nodes

This part of the experiment will add nodes to the hidden layer of the network. 5 groups of experiments are added 1-5 nodes in the hidden layer, each group of experiments is carried out 30 times, and the initial value of the weight of each experiment is a random number of [-1, 1]. The experimental data set is shown in Figure1, the experimental network structure is shown in Table 3, and the experimental results are shown in Figure 4.Table 3
Experimental network structure.
Network input dimensionNetwork structureNumber of hidden layer nodesNetwork structure after node trimming55-5-155-10-11010-10-1410-14-12020-15-1320-18-13030-20-1230-22-14040-25-1140-26-1Figure 4
Sensitivity comparison of adding hidden layer nodes.
(a)
5-4-1(b)
10-14-1(c)
20-18-1(d)
30-22-1(e)
40-26-1It can be seen from Figure4 that the structural sensitivity simulated by the algorithm in this paper is consistent with the real sensitivity trend due to the increase of the hidden layer. When the input dimension of the network gradually increases, this trend becomes more obvious, and the result of the simulation experiment is closer to the real sensitivity, which reflects the effectiveness and practicality of the algorithm in this paper.


## 4. The Construction of a School-Enterprise Cooperative Development Model of Higher Vocational Education Aided by Sensitive Neural Networks

This system adopts B/S framework to carry on the development and design of the information system. As long as the user is connected to the Internet, he can log in to the system and use some functions at the same time. In addition, the development of this system adopts a layered model. The advantage of layering is that it can effectively carry out cooperative development, and at the same time, it can provide software system reuse. Of course, scalability and maintainability will also be greatly improved. At the same time, the efficiency of software development will be greatly improved. For these reasons, the graphic information system adopts a layered development model to construct the environment. This hierarchical model uses the traditional three-tier hierarchical model, namely, the presentation layer, the data access layer, and the business logic layer. The schematic diagram of the related system architecture is shown in Figure5.Figure 5
System architecture diagram.The structure diagram of enterprise and position information management is shown in Figure6.Figure 6
Flow chart of enterprise and position information management.After constructing the above system model, the performance of the system model constructed in this paper is verified. And the experiments results are shown in Table4.Table 4
System performance verification.
NOManagement effectDevelopment planNOManagement effectDevelopment planNOManagement effectDevelopment plan174.990.81881.377.03575.081.6285.991.31982.790.03689.892.3392.083.52080.271.23779.987.3480.290.42189.470.73889.577.4574.680.92290.270.73978.181.4686.092.22375.092.94090.471.5782.573.52484.585.74182.187.0879.491.32583.478.74283.079.4991.191.12687.184.84378.789.61075.480.92783.079.84482.891.91189.972.62882.678.04575.377.21275.576.12981.475.24686.777.11384.981.73082.284.44780.573.71488.183.43191.973.64889.977.01582.486.13275.488.94983.979.61684.984.73377.193.45077.776.01777.391.13483.173.75186.471.5From the above experimental results, the school-enterprise cooperative development model of higher vocational education based on sensitive neural network proposed in this paper has good results.


## 5. Conclusion

Encouraged by active policies, such as the state’s support and guidance of social forces to invest in running schools, higher vocational education has developed rapidly in recent years, and the number of students at school has reached new highs, and it has become a new force in vocational education. Due to the influence of traditional concepts and the limitation of funds, higher vocational colleges still have a gap between the graduates they train and the talents needed by enterprises. Facing the large environment where state-run higher vocational colleges have launched school-enterprise cooperation, higher vocational colleges should use their own flexible and efficient mechanism advantages of private enterprises to better carry out school-enterprise win-win cooperation, ensure the quality of training, such a large number of students, and establish a scientific and standardized talent training system. This article changes the traditional analysis model of school-enterprise cooperation in higher vocational education, applies sensitive neural networks to the construction of the school-enterprise cooperation development model of higher vocational education, improves the intelligence of the development of school-enterprise cooperation in higher vocational education, and promotes the stable development of school-enterprise cooperation in higher vocational education.



---

*Source: 6323872-2022-04-23.xml*