Generation of Fuzzy Rules with Subtractive Clustering

Learning fuzzy rule-based systems with genetic algorithms can lead to very useful descriptions of several problems. fuzzy logic (fl) provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy or missing input information. the fl model is empirically based, relying on an operator's experience rather than their technical understanding of the system. in the fl method, any reasonable number of inputs can be processed and numerous outputs will be generated, although defining the rule-base quickly becomes complex if too many inputs and outputs are chosen for a single implementation since rules defining their interrelations must also be defined. this will increase the number of fuzzy rules and complexity but may also increase the quality of the control. many methods were proposed to generate fuzzy rules-base. the basic idea is to study and generate the optimum rules needed to control the input without compromising the quality of control. the paper proposed the generation of fuzzy rule base by subtractive clustering technique in takagi-sugeno-kang (tsk) fuzzy method for traffic signal control system.