DocumentCode
412621
Title
Truck backing up neural network controller optimized by genetic algorithms
Author
Ho, M.L. ; Chan, P.T. ; Rad, A.B. ; Mak, C.H.
Author_Institution
Dept. of Electr. Eng., Hong Kong Polytech. Univ., China
Volume
2
fYear
2003
fDate
8-12 Dec. 2003
Firstpage
944
Abstract
Jenkins-Yuhas network has been applied successfully to solve the trailer-truck backing up problem. Genetic algorithms (GA) is used to train the Jenkins-Yuhas network controller. The integration of genetic algorithms and neural networks training avoids complicated formulation of derivative function that required in conventional error back propagation techniques (gradient descent). In addition, it can avoid trapping in local minimum point. The performance of GA trained neural network controller is evaluated via simulation studies. It has been demonstrated that the controller can successfully control trailer-truck for different initial parking conditions, i.e. with same set of trained weights, to the loading dock.
Keywords
backpropagation; genetic algorithms; neurocontrollers; GA; Jenkins-Yuhas network controller; derivative function; error back propagation technique; genetic algorithm; loading dock; neural networks training; optimization; parking conditions; trailer-truck backing up problem; Control systems; Genetic algorithms; Mathematical model; Multi-layer neural network; Neural networks; Neurons; Nonlinear control systems; Optimization methods; System analysis and design; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN
0-7803-7804-0
Type
conf
DOI
10.1109/CEC.2003.1299768
Filename
1299768
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