Fuzzy Rule base Generation from Numerical Data using Biogeography-based Optimization
نویسندگان
چکیده
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 enumerate rules corresponding to each data set. The paper discusses rule extraction for type zero TSK fuzzy systems for battery charger. However, the approach is very powerful computation tool to deal with NP hard problems. The results indicate that the BBO is a very promising optimizing algorithm for evolving fuzzy logic based systems.
منابع مشابه
Fact Gathering Using Ant Colony Optimization
Fact Gathering means generating rule base from available numerical data or data base. The intelligence of a fuzzy system lies in its rule base. Generating rule base is one of the most important and difficult tasks when designing fuzzy systems. Various rule base generation methods are used such as Neural networks, genetic algorithms, biogeography based optimization approach, ant colony optimizat...
متن کاملDesigning fuzzy inference systems from data: An interpretability-oriented review
Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation...
متن کاملImprovement of Rule Generation Methods for Fuzzy Controller
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...
متن کاملKnowledge 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 fro...
متن کاملAutomatic Fuzzy Rule Base Generation from Numerical Data: Cuckoo Search Algorithm
Fuzzy Classifiers are an powerful class of fuzzy systems. Evolving fuzzy classifiers from numerical data has assumed lot of remarks in the recent past. This paper proposes a method of evolving fuzzy classifiers using a three step technique. In the first step, a modified Fuzzy C–Means Clustering technique is applied to generate membership functions. In the next step, rule base are generated usin...
متن کامل