Fuzzy rule based systems are one of the very important class of knowledge based systems. The knowledge in a fuzzy system is embedded in the form of a rule base. This short article presents a new approach to rule base extraction from numerical data using Biogeography Based Optimization Approach (BBO). The rule base extraction problem is formulated as the minimization problem. BBO was used to enu...

In this special issue on "Fuzzy Expert Systems," six papers cover a wide range of concerns--from theory to applications including: (1) a rule base reorganization, (2) a linear interpolation, (3) a neuro-fuzzy approach to pairwise comparison, (4) properties of reduction, in transitive matrices, (5) a consistency checking procedure, and (6) a context dependency model. We present a brief review of...

Neuro-fuzzy systems have recently gained a lot of interest in research and application. These are approaches that learn fuzzy systems from data. Many of them use rule weights for this task. In this paper we discuss the innuence of rule weights on the interpretability of fuzzy systems. We show how rule weights can be equivalently replaced by modiications in the membership functions of a fuzzy sy...

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...

This paper presents an evolutionary Multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler and still accurate linguistic fuzzy models by learning fuzzy inference operators and applying rule selection. The Fuzzy Rule Based Systems obtained in this way, have a better trade-off between interpretability and accuracy in ling...

This paper proposes fuzzy modeling using obtained data. Fuzzy system is known as knowledge-based or rule-bases system. The most important part of fuzzy system is rule-base. One of problems of generation of fuzzy rule with training data is inconsistence data. Existence of inconsistence and uncertain states in training data causes high error in modeling. Here, Probability fuzzy system presents to...

This paper presents a statistical approach for rule-base generation of handwriting recognition. The new proposed method integrates the heuristic feature selection with the statistical evaluation and thus improves the generation speed and the performance of the fuzzy rule-based handwriting recognition system. Fuzzy statistical measures are employed to identify relevant features from a given larg...

A fuzzy rule-based system consists of fuzzy if-then rules such as “If x1 is small and x2 is small than y is large”. The problem with existing fuzzy rule-based systems is that the size of the rule-base (number of rules) increases exponentially with the increase of the number of fuzzy sets involved in the rules. This exponential increase in size of the rule-base increases the search time and henc...

A fuzzy rule-based classification system (FRBCS) is one of the most popular approaches used in pattern classification problems. One advantage of a fuzzy rule-based system is its interpretability. However, we're faced with some challenges when generating the rule-base. In high dimensional problems, we can not generate every possible rule with respect to all antecedent combinations. In this paper...

In this paper performance of Puma 560 manipulator is being compared for hybrid gradient descent and least square method learning based ANFIS controller with hybrid Genetic Algorithm and Generalized Pattern Search tuned radial basis function based Neuro-Fuzzy controller. ANFIS which is based on Takagi Sugeno type Fuzzy controller needs prior knowledge of rule base while in radial basis function ...