Knowledge Extraction from Numerical Data : an Abc Based Approach

Fuzzy rule based systems provide a framework for representing & processing information in a way that resembles human communication & reasoning process. Two approaches can be found in the literature which is used for rule based generation; In Knowledge Driven Models the requisite rule base is provide by domain expert & knowledge engineers. In the Data Driven Models the rule base is generated from available numerical data. As the domain experts are difficult to find & knowledge extraction from the experts itself is difficult task the data driven modeling assume significance, One has to apply soft computing base methodology to generate rule base form data. Neural networks, genetic algorithm & particle swam optimization are some of the approaches [1]. Basic Artificial Bee Colony algorithm (ABC) has the advantages of strong robustness, fast convergence and high flexibility, fewer setting parameters, but it has the disadvantages premature convergence in the later search period and the accuracy of the optimal value which cannot meet the requirements sometimes [4]. KEY-WORDS: Artificial Bee Colony Algorithm, Fuzzy Membership Function, Sugeno System, Rule Based Generation.