用连续回归神经网络求解泛函极值问题

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Abstract In this paper, the continuous time recurrent neural network is proposed to solve the functional minimization problem, which is often involved in estimation and control. At first, the continuous time BPTT algorithm corresponding to the problem is presented. Then,an on-line algorithm based on the amendments of the BPTT algorithm is discussed. This on-line algorithm paves the way for parallel realization.

Key words： Functional minimization Continuous time recurrent neural network BPTT algorithm On-line learning algorithm

Received: 30 November 1998

PACS:	TN-052
	O177

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	Liu Heping
	Zhang Lanling
	Sun Yikang

Cite this article:

Liu Heping,Zhang Lanling,Sun Yikang. A CONTINUOUS TIME RECURRENT NEURAL NETWORK BASED METHOD TO SOLVE FUNCTIONAL MINIMIZATION PROBLEM[J]. , 2000, 22(5): 729-734 .

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http://jeit.ie.ac.cn/EN/ OR http://jeit.ie.ac.cn/EN/Y2000/V22/I5/729

[1]	Deng Chao^①; Xiong Fanlun^②. AN EFFICIENT ON-LIVE LEARNING METHOD FOR RADIAL BASIS FUNCTION NEURAL KETWORKS[J]. , 2001, 23(5): 472-478 .