Research on Detecting Method of Submarine Oil Pipelines Corrosion Degree Based on Chaos Genetic Algorithm Neural Network

2007 ◽  
Author(s):  
Meijuan Gao ◽  
Jingwen Tian ◽  
Kai Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Oil Pipelines ◽  
Chaos Genetic Algorithm ◽  
Detecting Method

Wavelet Neural Network Based on Chaos Genetic Algorithm

2013 ◽  
Vol 339 ◽  
pp. 307-312 ◽  
Author(s):  
Hu Cheng Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Learning Algorithm ◽  
Wavelet Neural Network ◽  
Optimal Parameters ◽  
Chaos Optimization ◽  
Chaos Optimization Algorithm ◽  
Learning Speed ◽  
Chaos Genetic Algorithm ◽  
Remarkable Progress

To improve the performance of Wavelet Neural Network (WNN), a hybrid WNN learning algorithm, which is combination of Genetic Algorithm (GA) and Chaos Optimization Algorithm (COA) in a mutual complementarity manner, is proposed. In the algorithm, GA is first used to roughly search the optimal parameters of WNN as a whole, and then COA is adopted to perform the refined search on the basis of the result obtained by GA, which can make remarkable progress in modeling accuracy, learning speed, and overcoming local convergence or precocity. Simulation show its effectiveness.

Intelligent Optimization of Optimal Operating Mode in the Production Process of Copper Converter

2014 ◽  
Vol 1022 ◽  
pp. 361-367
Author(s):  
Yi Yuan Shao ◽  
Wen Bin Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Operating Mode ◽  
Technological Parameters ◽  
Optimal Operating ◽  
Intelligent Optimization ◽  
Copper Converter ◽  
Operating Modes ◽  
On Line ◽  
Chaos Genetic Algorithm

In order to enhance the standard of copper converter operation,operating modes are employed to describe a group of operating parameters which need to be decided on line, and an intelligent optimization way based on neural network and improved chaos genetic algorithm for operating modes is presented. At first, the optimal samples is sieved from the historical sample set. Secondly, the functional relationship with optimization objective and technological parameters is drilled by a neural network model. At last, chaos genetic algorithm with chaotic variables is adopted to seek the optimal operating mode. This method is applied to real-time optimization of copper converter operating parameters.The running results show that the yield of converter raises by 5.9%, and the quantity of the disposed cool materials increases by 7.7%.

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

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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