Abstract
Nano electronics is one of the engineering system application which used to reduce the human efforts with optimized way. During the electronic system implementation process human reasoning, decision making and mathematical approaches plays an important role which is obtained by the fuzzy logics. At the same time the medical image segmentation need more accureate segmentation process. So, this paper presents a novel optimized automatic generation of fuzzy rules for nonlinear system based on a subtractive clustering algorithm for implementing the nanoelectronic system. The proposed fuzzy logic approach uses the output studied signal as the input while preparing the decision logic for medical segmentation. A multiple objective real-valued genetic algorithm is employed for optimization of a fuzzy model by fine-tuning the constrained parameters of subtractive clustering process. The output of the real system varies to achieve all possible values of a fuzzy input control signal to address the problem of an imbalanced signal. A mechanical disturbance has been used as a validation control signal. The fitness function includes both training and validation control signal of the studied system are used to avoid signal over-fitting in given image. The root mean square error measures are incorporated into genetic algorithm as an objected function to achieve an accurate model of the fuzzy system. The approach that is suggested is for the application of a synchronized generator which has then been attached to an infinite bus and equipped with static VAR (Volt-Ampere Reactive) compensator. Then the performance metrics such as time simulation, performance index values are used to estimate the effectiveness of the proposed approache is with regard to settling time, overshoot and undershoot.