In this paper, we present an evolutionary multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference operators together with Rule Base. The Multiobjective Evolutionary Algorithm proposed generates a set of Fuzzy Rule Based Systems with diff...

This work proposes a novel approach for the automatic generation and tuning of complete Takagi-SugenoKang fuzzy rule based systems. The examined system aims to explore the effects of a reduced search space for a genetic programming framework by means of grammar guidance that describes candidate structures of fuzzy rule based systems. The presented approach applies context-free grammars to gener...

Neuro-fuzzy system (NFS) and especially localized NFS are powerful rule-based methods for knowledge extraction, capable of inducing salient knowledge structures from data automatically. Contemporary localized NFSs, however, often demand large features and rules to accurately describe the overall domain data, thus degrading their interpretability and generalization traits. In light of these issu...

This paper presents a layer-structured fuzzy neural network (FNN) for learning rules of fuzzy-logic control systems. Initially, FNN is constructed to contain all the possible fuzzy rules. We propose a two-phase learning procedure for this network. The first phase is a error-backprop (EBP) training, and the second phase is a rule pruning. Since some functions of the nodes in the FNN have the com...

Designing classifiers may follow different goals. Which goal to prefer among others depends on the given cost situation and the class distribution. For example, a classifier designed for best accuracy in terms of misclassifications may fail when the cost of misclassification of one class is much higher than that of the other. This paper presents a decision-theoretic extension to make fuzzy rule...

In this paper a linearized Heffron-Philips model of a Single Machine Infinite Bus power system with a Fuzzy Logic Power System Stablizer (PSS) is developed. The designed fuzzy-based PSS adjusts two inputs by appropriately processing of the input angular speed and angular acceleration signal, and provides an efficient damping. The behavior of the SMIB system with & without PSS has been compared/...

Data-driven fuzzy modeling has been used in a wide variety of applications. However, in fuzzy rule-based models acquired from numerical data, redundancy often exists in the form of redundant rules or similar fuzzy sets. This results in unnecessary structural complexity and decreases the interpretability of the system. In this paper, a rule-base self-extraction and simpli&cation method is propos...