BP Neural Network Evaluation Model Research Articles

Is it Feasible to Use BP Neural Network for Academic Journal Evaluation: an Attempt Based on 6 Single Objective Weighting Methods

ABSTRACTIn the new era, the analysis of academic journal evaluation methods and the comprehensive comparison of the advantages, disadvantages, and stability of different methods can help to provide some reference for academic journal evaluation. In this study, single model evaluation was carried out for 6 weighting methods without comprehensive evaluation value, and fuzzy comprehensive evaluation is performed on the results passing the nonparametric test. Based on the evaluation, BP neural network is introduced, and BP neural network evaluation model is established. The results show that the fuzzy Borda evaluation can integrate the evaluation value and evaluation order of single models, and has higher accuracy compared with single evaluation models. The prediction rate of the network model based on the gradient descent optimization algorithm can reach more than 80%, and the weights obtained from the continuous self‐learning of the neural network training set can reduce the subjectivity and mutual interference between indicators.

Coal Mine Safety Evaluation Based on Machine Learning: A BP Neural Network Model.

As the core of artificial intelligence, machine learning has strong application advantages in multi-criteria intelligent evaluation and decision-making. The level of sustainable development is of great significance to the safety evaluation of coal mining enterprises. BP neural network is a classical algorithm model in machine learning. In this paper, the BP neural network is applied to the sustainable development level decision-making and safety evaluation of coal mining enterprises. Based on the analysis of the evaluation method for sustainable development of coal enterprises, the evaluation index system of sustainable development of coal enterprises is established, and a multi-layer forward neural network model based on error backpropagation algorithm is constructed. Based on the system theory of man, machine, environment, and management, and taking the four single elements and the whole system in a coal mine as the research object, this paper systematically analyzes and studies the evaluation and continuous improvement of coal mine intrinsic safety. The BP neural network evaluation model is used to analyze and study the intrinsic safety of coal mines, the shortcomings of the intrinsic safety construction of coal mines are found, and then improvement measures are put forward to effectively promote the safe production of coal mines and finally realize the intrinsic safety goal of the coal mine.

Ablation state assessment of SF6 circuit breaker contacts based on BP neural network and mean impact value

In order to evaluate the ablation state of the SF6 circuit breaker contacts, simulated ablation tests were carried out on the circuit breaker, and the dynamic resistance curve as a function of contact travel was measured with three levels of injected current. The results showed that the increasing of test current value can reduce the fluctuation of the dynamic resistance curve as a function of contact travel. In addition to the ablation effect of current, the loosening of the operating mechanism will affect the accuracy of opening and closing action of the contact, resulting in the continuous reduction of the main contact travel in the dynamic resistance curve as a function of contact travel. Based on the measurement results, a BP neural network evaluation model was built to evaluate the ablation state of the contact. Seven parameters were extracted from the dynamic resistance curve as a function of contact travel as model inputs, and mean impact value algorithm was used to optimize the model. After the optimization, the average relative error of the evaluation to the verification set is less than 10.4%.

BP neural network model for comprehensive evaluation of rural financial ecological environment

For this reason, the key to solve the problem of rural financial development is to solve the problem of rural financial development. At present, the state has given important instructions to improve the rural financial ecological environment, but the relevant research on the evaluation of rural financial ecological environment in China is still insufficient. In view of this situation, this paper puts forward a BP neural network model for the comprehensive evaluation of rural financial ecological environment. First of all, this paper studies the relevant basic theory of financial ecology and ecological environment comprehensive evaluation. Through the research, this paper believes that the construction of rural financial ecological environment involves many factors, and each factor has a mutual influence. It is difficult to determine the influence of a single factor on the final result. Therefore, in view of this complex situation, this paper establishes a set of multi factor evaluation index systems including economy, policy, law, culture, etc. And these complex factors are trained by BP neural network. The training results were normalized to quantify the specific impact of each index on the rural financial ecological environment. Finally, in order to verify the actual evaluation effect of this model, a number of comparative experiments including validity verification, stability analysis, comparison and verification of different model error rates are carried out. Through the analysis of experimental data, we can see that the BP neural network evaluation model in this paper has good comprehensive performance and significantly improves the calculation accuracy compared with the traditional analytic hierarchy process.

Effectiveness evaluation of underwater cluster based on improved RS-BP

In the modern underwater confrontation environment, efficient and accurate underwater cluster warfare effectiveness evaluation is essential. In order to solve the problem that the evaluation efficiency of the neural network evaluation model decreases with the expansion of the combat index system, the method of combining the rough set(RS) data reduction and BP neural network is used to construct the rough set reduction BP (RS-BP) neural network evaluation model to reduce the network input layer neuron quantity, optimize the initial weight learning of the neural network. The simulation results show that the convergence speed and evaluation accuracy of the RS-BP neural network model are improved.

Research on BP model optimization of PSO algorithm based on Computational Mathematics

Construction of the quality and safety evaluation model for dairy products is significant to ensure food safety for people. In the case that there is a big error in the BP neural network quality and safety evaluation model for dairy products, this paper put forward the optimization method based on the PSO algorithm to reduce the output error of the BP neural network evaluation model through optimizing the weights and thresholds of BP neural network. By simulation of optimizing the BP evaluation model through specific data, experiments showed that compared with the standard BP evaluation model and standard BP evaluation, the optimized BP evaluation model is greatly improved in output error.

BP neural network study on the quality evaluation system of innovation and entrepreneurship education in local universities

When the country vigorously advocates the goal of cultivating innovative and entrepreneurial engineering talents, the quality evaluation of innovation and entrepreneurship education has naturally become a part of the higher education system. However, there are few studies on the quality evaluation of innovation and entrepreneurship education in engineering majors in China. Firstly, through literature research, combined with the current development trend of quality evaluation of innovation and entrepreneurship education at home and abroad, BP neural network-based evaluation method was selected. Secondly, according to the three-element theory of education, namely, the three factors of educators, educators and education, and comprehensive research, the content of quality evaluation of innovation and entrepreneurship education in engineering majors in colleges and universities is determined, that is, from professional links, teaching links and students. Levels to build evaluation indicators. After establishing the evaluation index system, this paper uses Chengdu University of Technology as a research sample to issue questionnaires for engineering students in Chengdu University of Technology to obtain data for use, and to analyze and analyze the data by AHP, so as to obtain the weight of each indicator. Finally, based on the data collected by the questionnaire, the BP neural network data fitting and model training and optimization are carried out to determine the feasibility of the quality evaluation index of innovation and entrepreneurship education in engineering majors in colleges and universities, and to establish BP neural network evaluation model for engineering majors. The innovation and entrepreneurship education quality evaluation system provides new research methods and research ideas, enriches the connotation and breadth of the quality evaluation system of innovation and entrepreneurship education in engineering majors, and makes up for the shortcomings and vacancies in related fields.

Comprehensive evaluation of urban livability

This article is based on the comprehensive evaluation of urban livability. This article selects the indicators to measure the urban livability, and uses the factor analysis method to reasonably screen the indicators, and establishes an improved BP neural network evaluation model to obtain the livability of different cities in Shaanxi Province. Monte Carlo simulation is used to analyze the impact of different indicators on urban livability. Then this article comprehensively considers the uncertain factors, establishes a predictive model of stochastic differential equations, and establishes a dynamic evaluation model to reassess the livability of cities.

Location Decision of Logistics Distribution Centers Based on Artificial Neural Network

With the never-ending changes and improvements of science and technology and the rapid development of economy, modern logistics plays an increasingly important role in social and economic development. As an important link between enterprises and consumers, logistics distribution is critical in the smooth development of logistics activities, and the reasonable location of logistics distribution centers is directly related to the distribution efficiency, logistics cost and consumer satisfaction of enterprises. Therefore, this paper studies the location decision of logistics distribution centers, and with the aid of artificial neural network, which can simulate the neural network of human brain and reproduce the process of brain thinking decision, establishes the BP neural network evaluation model, and evaluates the merits and demerits of several location plans with examples. The results show that the method based on artificial neural network is effective and practical, and provides a new idea for the location decision of logistics distribution centers.

Enhanced perceptual psychopathology correlation mechanism model based on rough set and neural network

Aimed at sensibility—psychopathology problem, undergraduates are selected to be research object and a correlative mechanism model for rough set and neural network of reinforced sensibility—psychopathology is proposed. Firstly, five characteristics are taken as example to analyze common psychological illnesses of undergraduates. These five characteristics include: behavior, emotional state, diet and sleep, character trait and physical illness, and four common psychological illnesses of undergraduates are selected as training to construct BP neural network evaluation model; then, upper and lower approximation of rough set and boundary region theory are adopted for noise removal of noise sample, which can solve sample noise interference of BP network pattern recognition effectively and reduce error recognition rate of BP network significantly. Finally, performance advantage of algorithm is verified through simulation experiment.

Application of Genetic BP Neural Network in Safety Comprehensive Evaluation of Tailing

Based on safety assessment factors determined by operation characteristics of a certain tailing ,genetic BP neural network evaluation model is established. To overcome such problems of BP neural network as slow convergence ,poor generalization ability and easy to fall into local minimum value,this paper proposes to use genetic algorithm to optimize threshold value,weights and structure of neural network. Thus,by taking advantage of extensive mapping ability of neural network and global search ability of genetic algorithm,neural network and genetic algorithm will have complementary advantages and the learning speed of network will be accelerated. The application of the described method shows optimized fitting precision,improved accuracy and efficiency ,and enhanced generalization ability of BP neural network. In conclusion,this model can effectively reflect and accurately evaluate non-linear relations between security levels and evaluation factors in tailing.

Electric vehicle industry development environment evaluation in China based on BP neural network

As an emerging industry of energy conservation and environment protection, electric vehicle industry has broad prospects for development. But now, electric car industry is in the initial stage and the development environment is complex, so it is very necessary to study the development environment. This paper combines electric vehicle industry development environment evaluation index system with BP neural network to establish the electric vehicle industry evaluation model. Then the indicator score values as training samples obtained from the use of experts scoring method are imported to the BP neural network evaluation model. After training and testing the neural network, this paper compares the testing results with the results based on analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method, to test the validity of the evaluation model. The result shows that evaluation model based on BP neural network can effectively improve the reliability and accuracy of evaluation result.

A Neural Network Model for Expressway Investment Risk Evaluation and its Application

Investment risk evaluation of expressways plays a significant role in the sustainable development of expressways. A consideration of the inconsistency between operation accuracy and operation efficiency of the traditional quasi-3-layer BP neural network evaluation model is first established. This paper proposes and designs a variable-structure neural network model in the random re-linking process from the input layer neurons to the hidden layer neurons. Then, the model acquires the hidden layer to the output layer from the angle of structural optimization. Secondly, in view of the characteristics of the Chinese expressway investment, this paper develops an expressway project investment risk evaluation index system. Furthermore, a design of the expressway project investment risk evaluation model is completed based on the variable-structure neural network of the re-linking random process. In addition, the model has been verified with ten Chinese expressway projects. The risk evaluations have been conducted for four of the ten expressway projects. The research result shows that the average relative error predicted with such model is 1.91% and the maximum relative error is 2.63%. Therefore, the prediction result is deemed suitable.