Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/46438
標題: Cascade steepest descendant learning algorithm for multilayer feedforward neural network
作者: Wang, G.J.
王國禎
Chen, J.J.
關鍵字: multilayer feedforward neural network;parameter self-tuning learning;injection molding temperature control;dynamical-systems;identification
Project: Jsme International Journal Series C-Mechanical Systems Machine Elements and Manufacturing
期刊/報告no:: Jsme International Journal Series C-Mechanical Systems Machine Elements and Manufacturing, Volume 43, Issue 2, Page(s) 350-358.
摘要: 
In this article, a new and efficient multilayer neural networks learning algorithm is presented. The key concept of this new algorithm is the two-stage implementation of the steepest descendant method. At the first stage, the steepest descendant method is used to search the optimal learning constant eta and momentum term alpha for each weights updating process. At the second stage, the Delta learning rule is then employed to modify the connecting weights in terms of the optimal eta and alpha. Computer simulations show that the proposed new algorithm outmatches other learning algorithms both in converging speed and success rate. On real industrial application, we first apply the new neural network learning algorithm to the identification of a highly nonlinear injection molding barrel system. Experimental results demonstrate that the new algorithm can precisely identify the complicate injection molding barrel system. Further more, a self-tuning PID controller for precise temperature control based on the trained neural network barrel model is developed. Real experiments show that the self-tuning PID controller can precisely control the barrel temperature within +/-0.5 degrees C.
URI: http://hdl.handle.net/11455/46438
ISSN: 1340-8062
Appears in Collections:生醫工程研究所

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