Resilient back propagation learning algorithm for recurrent fuzzy neural networks

Paris Mastorocostas

doi:10.1049/el:20040052

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Resilient back propagation learning algorithm for recurrent fuzzy neural networks

Paris Mastorocostas

Year: 2004 Journal: Electronics Letters Vol: 40 (1)Pages: 57-58 Publisher: Institution of Engineering and Technology

DOI: 10.1049/el:20040052

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Abstract

An efficient training method for recurrent fuzzy neural networks is proposed. The method modifies the RPROP algorithm, originally developed for static neural networks, in order to be applied to dynamic systems. A comparative analysis with the standard back propagation through time is given, indicating the effectiveness of the proposed algorithm.

Keywords:

Rprop Artificial neural network Backpropagation Computer science Algorithm Types of artificial neural networks Recurrent neural network Artificial intelligence Fuzzy logic Neuro-fuzzy Time delay neural network Fuzzy control system

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Neural Networks and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Algorithms and Applications

Physical Sciences → Engineering → Control and Systems Engineering

Fuzzy Logic and Control Systems

Physical Sciences → Computer Science → Artificial Intelligence

Resilient back propagation learning algorithm for recurrent fuzzy neural networks

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