Evolving Fuzzy Rule-Based Models

Abstract

An approach of evolving fuzzy rule-based models by genetic algorithms (GA) is proposed in the paper. Both structure of the model (fuzzy rules) and parameters of the fuzzy membership functions of the linguistic variables are generated automatically. The main feature of the proposed approach is the new encoding mechanism of the chromosome that is more efficient than encoding used in previous evolutionary learning methods. The representation does not need all the rules to be present because the GA selects a small subset of the used rules only. This fact leads to minimizing the computational load using significantly smaller chromosome and real-coded GA, making possible simultaneous parameter and structural identification. Evolving fuzzy rule-based models need only inputs and outputs to be known, but unlike the other typical black-box models'(neura1 networks, polynomial models etc.) their transparency is very high due to the design of linguistic rules during the process of knowledge extraction and aggregation. Two practical building services engineering problems are considered in order to illustrate the applicability of the approach.