Fast and Parsimonious Self-Organizing Fuzzy Neural Network

Publish Year: 1388
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:

CSICC14_068

تاریخ نمایه سازی: 24 خرداد 1388

Abstract:

This paper introduces a revisited hybrid algorithm for function approximation. In this paper, a simple and fast learning algorithm is proposed, which automates structure and parameter identification simultaneously based on input-target samples. First, without need of clustering, the initial structure of the network with the specified number of rules is established, and then a training process based on the error of other training samples is applied to obtain a more precision model. After the network structure is identified, an optimization learning, based on the criteria error, is performed to optimize the obtained parameter set of the premise parts and the consequent parts. At the end, comprehensive comparisons are made with other approaches to demonstrate that the proposed algorithm is superior in term of compact structure, convergence speed, memory usage and learning efficiency.

Authors

Omid khayat

Dept. of Nuclear Engineering Amirkabir university

Javad Razjouyan

Dept. of Biomedical Engineering Amirkabir University

Hadi ChahkandiNejad

Dept. of Electrical Engineering Azad University of Gonabad

Mahdi Mohammad Abadi

Dept. of Electrical Engineering Azad University of Gonabad