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Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS)

Publication Type : Journal Article

Publisher : IEEE Transactions on Neural Networks

Source : IEEE Transactions on Neural Networks, Volume 16, Number 1, p.160-174 (2005)

Keywords : Algorithms, Artificial intelligence, asymmetric Gaussian membership functions, asymmetric subsethood-product fuzzy neural inference system, Automated, cluster analysis, Computer networks, Computer science, Computer-Assisted, Computing Methodologies, Councils, fuzzy if-then rules, Fuzzy logic, Fuzzy neural networks, fuzzy set theory, Fuzzy sets, Fuzzy systems, Gaussian processes, government, gradient descent learning framework, gradient methods, Inference algorithms, inference mechanisms, Information Storage and Retrieval, learning (artificial intelligence), linguistic inputs, Models, Multilayer perceptrons, mutual subsethood, Neural Networks (Computer), Numerical Analysis, Pattern recognition, Physics, product conjunction, Signal processing, Statistical, supervised gradient descent learning, volume defuzzification, weight fuzzy sets

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2005

Abstract : This work presents an asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS) that directly extends the SuPFuNIS model by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood-product network admits both numeric as well as linguistic inputs. Input nodes, which act as tunable feature fuzzifiers, fuzzify numeric inputs with asymmetric Gaussian fuzzy sets; and linguistic inputs are presented as is. The antecedent and consequent labels of standard fuzzy if-then rules are represented as asymmetric Gaussian fuzzy connection weights of the network. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. Despite the increase in the number of free parameters, the proposed model performs better than SuPFuNIS, on various benchmarking problems, both in terms of the performance accuracy and architectural economy and compares excellently with other various existing models with a performance better than most of them.

Cite this Research Publication : Dr. Shunmuga Velayutham C. and Kumar, S., “Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS)”, IEEE Transactions on Neural Networks, vol. 16, pp. 160-174, 2005.

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