Intelligent Planning of Ship Route in Complex Waters Based on Chaos Genetic Algorithm

2021 ◽  
Author(s):  
Lixiao Jia ◽  
Jiantao Wang ◽  
Yunlong Guo
Keyword(s):  
Genetic Algorithm ◽  
Ship Route ◽  
Chaos Genetic Algorithm ◽  
Intelligent Planning

Chaos-Genetic Algorithm for the System Identification of a Small Unmanned Helicopter

2012 ◽  
Vol 67 (3-4) ◽  
pp. 323-338 ◽  
Author(s):  
Tianmiao Wang ◽  
Yang Chen ◽  
Jianhong Liang ◽  
Yongliang Wu ◽  
Chaolei Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
System Identification ◽  
Unmanned Helicopter ◽  
Chaos Genetic Algorithm ◽  
Small Unmanned Helicopter

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.

Seismic Wavelet Estimation Using High-Order Statistics and Chaos-Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 4241-4247 ◽  
Author(s):  
Hong Tao Sun ◽  
Yong Shou Dai ◽  
Fang Wang ◽  
Xing Peng
Keyword(s):  
Genetic Algorithm ◽  
Signal To Noise Ratio ◽  
Linear Optimization ◽  
Optimization Methods ◽  
High Signal ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
Convergence Performance ◽  
High Order Statistics ◽  
Chaos Genetic Algorithm

Accurate and effective seismic wavelet estimation has an extreme significance in the seismic data processing of high resolution, high signal-to-noise ratio and high fidelity. The emerging non-liner optimization methods enhance the applied potential for the statistical method of seismic wavelet extraction. Because non-liner optimization algorithms in the seismic wavelet estimation have the defects of low computational efficiency and low precision, Chaos-Genetic Algorithm (CGA) based on the cat mapping is proposed which is applied in the multi-dimensional and multi-modal non-linear optimization. The performance of CGA is firstly verified by four test functions, and then applied to the seismic wavelet estimation. Theoretical analysis and numerical simulation demonstrate that CGA has better convergence speed and convergence performance.

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