Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Computer
IEEE Spectrum
International Journal of Approximate Reasoning
Neural network implementation of fuzzy logic
Fuzzy Sets and Systems
Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
On the principles of fuzzy neural networks
Fuzzy Sets and Systems
A fuzzy neural network for rule acquiring on fuzzy control systems
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Industrial Applications of Fuzzy Control
Industrial Applications of Fuzzy Control
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Transductive knowledge based fuzzy inference system for personalized modeling
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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Earlier we proposed a connectionist implementation of compositional rule of inference (COI) for rules with antecedents having a single clause. We first review this net, then generalize it so that it can deal with rules with antecedent having multiple clauses. We call it COIN, the compositional rule of inferencing network. Given a relational representation of a set of rules, the proposed architecture can realize the COI. The outcome of COI depends on the choice of both the implication function and the inferencing scheme. The problem of choosing an appropriate implication function is avoided through neural learning. COIN can automatically find a 'good' relation to represent a set of fuzzy rules. We model the connection weights so as to ensure learned weights lie in [0,1]. We demonstrate through extensive numerical examples that the proposed neural realization can find a much better representation of the rules than that by usual implication and hence results in much better conclusions than the usual COI.