A Research on the Optimal Design of BP Neural Network Based on Improved GEP

Wang, Ruliang; Zha, Benbo

doi:10.1142/s0218001419590079

Cited by 10 publications

(10 citation statements)

References 7 publications

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“…Increasing the number of layers of the neural network is beneficial to improving the output accuracy of the model, but it also increases the training time of the network. Nodes between adjacent layers of the BP network are interconnected, but nodes at the same layer are not [6]. Here, the three-layer neural network structure with a topology of 1 × 1 × 1 is taken as an example, as shown in Figure 1.…”

Section: Bp Neural Networkmentioning

confidence: 99%

“…en the error e corresponding to different samples was calculated with (6), and all the errors were summarized to obtain the total error of the network. e error signal δ of the hidden and output layers is calculated and the weight adjustment values Δw ho and Δw ih are obtained according to (10) and (11), and then the new weight matrix of w ih and who is obtained.…”

Section: E Bp Neural Network Algorithmmentioning

confidence: 99%

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Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Zhu

2022

Security and Communication Networks

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The traditional art education analysis and prediction are mainly based on the evaluation of human supervisors, and the evaluation results given by different evaluators vary greatly. The traditional analysis and prediction is difficult to reflect the objectivity and fairness of art education evaluation. This paper proposes a new idea of using the BP neural network model to analyze and forecast art education to improve the objectivity and impartiality of art education evaluation by big data. The work first constructed the art education evaluation index system covering the content of art education comprehensively and extensively. Then the BP neural network of art education analysis and prediction model was established. According to the content of art education evaluation system, the BP neural network’s input vector contains 30 evaluation indexes that affect art education, and its output vector is the art education evaluation results. The BP neural network with the structure of 30 × 10 × 1 was trained using the collected data. Finally, the work verified the scientificity of the evaluation model. The results of empirical analysis show that the established art education evaluation index system is reasonable and can reflect the artistic level of students, and the model of BP neural network has reliability in the analysis and prediction of art education big data and can objectively evaluate the level of art education.

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Section: Bp Neural Networkmentioning

confidence: 99%

Section: E Bp Neural Network Algorithmmentioning

confidence: 99%

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Zhu

2022

Security and Communication Networks

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“…As the most commonly used machine algorithm, neural network is widely used in various research fields. Among them, BP neural network [19,20] has the widest application range. In this section, the author studies its prediction effect by introducing a branching algorithm of neural network, Elman neural network.…”

Section: Elman Networkmentioning

confidence: 99%

Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

Gong

2022

Security and Communication Networks

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Video artworks are closely linked with the development of contemporary technology. Therefore, it is widely used in various fields of social life. Video art has become one of the main media forms of contemporary art. In the practice of art teaching, how to combine the existing content of traditional art teaching with video technology and how to understand the inner connection between traditional aesthetics and technological aesthetics have become issues that workers in the new era must think about and pay attention to. As a typical case of influencing works of art, movie animation is loved by the majority of young people. In order to quantify the application effect of machine learning in video art and film animation text mining, this paper conducts prediction research and analysis on several main aspects of color features involved in film animation. By introducing three typical machine learning methods, this paper analyzes the distribution law of the color features of film animation from the perspective of machine learning and its influence on artistic texts. Specifically, the paper uses machine learning methods as a carrier to predict the performance of multiple main modules of color features in movie animation. The prediction results show that the square of the correlation coefficient corresponding to the extreme learning machine is the largest, and the root mean square error, the mean absolute percentage error, and the median absolute error are the smallest, which shows that the extreme learning machine has the best prediction effect. Therefore, it corresponds to the best prediction. In addition, the comparison between the predicted data and the measured data shows that the relationship between the two is approximately a linear function of y = x. At the same time, the fitting calculation shows that the predicted data corresponding to the two main modules of the main color and the color structure in the color feature exhibit a good functional relationship of polynomial functions.

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“…We provide a guide to how the EAs can generate the best NN architecture. NAS based on GA [77,78] could use a binary string as a representation of the architecture, whereas NAS based on genetic programming (GP) [49,50] used a tree-based representation with nodes representing operations. NAS based on evolutionary programming (EP) [53] used continuous or discrete representations.…”

Section: Major Eas For Nasmentioning

confidence: 99%

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Liu,

Li,

Zhao

et al. 2024

CMES

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Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the use of EAs for NAS. In this paper, we summarize and explore the use of EAs for NAS, as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods. NAS performs well in healthcare applications, such as medical image analysis, classification of disease diagnosis, and health monitoring. EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task. Deep neural network has been successfully used in healthcare, but it lacks interpretability. Medical data is highly sensitive, and privacy leaks are frequently reported in the healthcare industry. To solve these problems, in healthcare, we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection. Moreover, we also point out future research directions for evolutionary NAS. Overall, for researchers who want to use EAs to optimize NNs in healthcare, we analyze the advantages and disadvantages of doing so to provide detailed guidance, and propose an interpretable privacy-preserving framework for healthcare applications.

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A Research on the Optimal Design of BP Neural Network Based on Improved GEP

Cited by 10 publications

References 7 publications

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Big Data’s Analysis and Prediction Method of Art Education Based on the BP Neural Network

Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

Evolutionary Neural Architecture Search and Its Applications in Healthcare

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