Application of Gauss Mutation Genetic Algorithm to Optimize Neural Network in Image Painting Art Teaching

With the continuous application of the art industry in various fields, more and more people choose to systematically learn the knowledge of the art industry. In the art major, image painting is one of the important contents of the art major. How to improve students’ aesthetic quality and comprehensive professional quality is studied, in which the content learning of image painting art is the key. Therefore, we have carried out technical exploration and result analysis based on Gaussian mutation genetic algorithm to optimize the application of neural network in image painting art teaching. We use Gaussian mutation genetic algorithm to study the neural network optimized teaching cloud platform technology. Compared with the traditional algorithm, the algorithm proposed in this paper has more funny computational efficiency, being able to comprehensively evaluate and improve students’ aesthetic quality and comprehensive professional quality. Gaussian mutation genetic algorithm can effectively improve the knowledge search ability of the platform and the running speed of the teaching platform. In the future research in the field of art industry, neural network will optimize the teaching cloud platform technology, which has laid a solid foundation for improving students’ aesthetic quality and comprehensive professional quality.

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Variable Chromosome Genetic Algorithm for Structure Learning in Neural Networks to Imitate Human Brain

Applied Sciences ◽

10.3390/app9153176 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3176 ◽

Cited By ~ 6

Author(s):

Kang-moon Park ◽

Donghoon Shin ◽

Sung-do Chi

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Structure Learning ◽

Structural Parameters ◽

Future Research ◽

Xor Problem ◽

Chromosome Attachment ◽

Artificial Neural Network Ann ◽

Exclusive Or

This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly been designed on the basis of heuristic knowledge of an artificial intelligence (AI) expert. To overcome this limitation, in this study evolutionary approach (EA) has been utilized to automatically generate the proper artificial neural network (ANN) structures. VCGA has a new genetic operation called a chromosome attachment. By applying the VCGA, the initial ANN structures can be flexibly evolved toward the proper structure. The case study applied to the typical exclusive or (XOR) problem shows the feasibility of our methodology. Our approach is differentiated with others in that it uses a variable chromosome in the genetic algorithm. It makes a neural network structure vary naturally, both constructively and destructively. It has been shown that the XOR problem is successfully optimized using a VCGA with a chromosome attachment to learn the structure of neural networks. Research on the structure learning of more complex problems is the topic of our future research.

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The preparation process of nanocomposite materials was optimized based on neural network and genetic algorithm

Functional materials ◽

10.15407/fm26.03.615 ◽

2019 ◽

Vol 26 (3) ◽

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Nanocomposite Materials ◽

Preparation Process

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Based on genetic algorithm to optimize learn vector quantization neural network is a new algorithm of distribution network fault line selection

Information Science and Management Engineering II (Set) ◽

10.2495/isme20140241 ◽

2014 ◽

Author(s):

Yusheng Zhou ◽

Pai Peng ◽

Yunlong Gao ◽

Zhengzhou An ◽

Rangjiao Liu

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Vector Quantization ◽

Distribution Network ◽

Fault Line ◽

Fault Line Selection ◽

Line Selection ◽

Network Fault

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Shunt Control on Smart Structures Using Genetic Algorithm and Neural Network Method.

10.26678/abcm.diname2019.din2019-0137 ◽

2019 ◽

Author(s):

Venicio Silva Araujo ◽

Guilherme Silva Prado ◽

Heinsten Frederich Leal dos Santos

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Smart Structures ◽

Neural Network Method ◽

Network Method ◽

Shunt Control

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Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

Recent Patents on Mechanical Engineering ◽

10.2174/2212797613999200730211514 ◽

2020 ◽

Vol 13 ◽

Author(s):

Renqiang Wang ◽

Qinrong Li ◽

Shengze Miao ◽

Keyin Miao ◽

Hua Deng

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Sliding Mode Control ◽

Intelligent Control ◽

Control Algorithm ◽

Sliding Mode ◽

Rbf Neural Network ◽

Input Saturation ◽

Ship Motion ◽

Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

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