A recurrent fuzzy cellular neural network system with automatic structure and template learning
Chin-Teng Lin
Chun-Lung Chang
Wen-Chang Cheng
Dept. of Electr. & Control Eng., Nat. Chiao-Tung Univ., Hsinchu, Taiwan;
This paper appears in: Circuits and Systems I: Regular Papers, IEEE Transactions on
Publication Date: May 2004
Volume: 51,
Issue: 5
On page(s): 1024- 1035
ISSN: 1549-8328
INSPEC Accession Number: 7999522
Digital Object Identifier: 10.1109/TCSI.2004.827622
Current Version Published: 2004-05-10
Abstract
It is widely accepted that using a set of cellular neural networks (CNNs) in parallel can achieve higher level information processing and reasoning functions either from application or biologics points of views. Such an integrated CNN system can solve more complex intelligent problems. In this paper, we propose a novel framework for automatically constructing a multiple-CNN integrated neural system in the form of a recurrent fuzzy neural network. This system, called recurrent fuzzy CNN (RFCNN), can automatically learn its proper network structure and parameters simultaneously. The structure learning includes the fuzzy division of the problem domain and the creation of fuzzy rules and CNNs. The parameter learning includes the tuning of fuzzy membership functions and CNN templates. In the RFCNN, each learned fuzzy rule corresponds to a CNN. Hence, each CNN takes care of a fuzzily separated problem region, and the functions of all CNNs are integrated through the fuzzy inference mechanism. A new online adaptive independent component analysis mixture-model technique is proposed for the structure learning of RFCNN, and the ordered-derivative calculus is applied to derive the recurrent learning rules of CNN templates in the parameter-learning phase. The proposed RFCNN provides a solution to the current dilemma on the decision of templates and/or fuzzy rules in the existing integrated (fuzzy) CNN systems. The capability of the proposed RFCNN is demonstrated on the real-world defect inspection problems. Experimental results show that the proposed scheme is effective and promising.
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