A hybrid method combining an evolutionary search strategy, interval mathematics and pole assignment-based closed-loop control synthesis is proposed to design a robust TSK fuzzy controller. The design objective is to minimize the number of linear controllers associated with rule conclusions and tune the triangular-shaped membership function parameters of a fuzzy controller to satisfy stability a...

In this study, a black box modeling of the coupledtank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutiona...

The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization problems. In the proposed GA, values in the genotypes are not real numbers but fuzzy numbers. Evolutionary processes in GA are extended so that GA can handle genotype instances with fuzzy numbers. The proposed method is applied to evolving neural networks with fuzzy weights and biases. Experimental ...

This paper describes a genetic-fuzzy approach for controlling a nonlinear model of the HIV immunology. The approach is set up to find mamdani fuzzy controllers capable of boosting the immune response while reducing the systemic cost to the body due to the use of high efficacy drugs. General aspects of the genetic fuzzy system are described while special emphasis is given to control results. The...

This paper describes a genetic programming approach to the construction of fuzzy classification system with if-then fuzzy rules. Recently many research studies were focusing on utilisation of evolutionary techniques for automatically extracting fuzzy rules from data. In this paper we present a method based on genetic programming with a special structure preserving representation and special rul...

This research introduces and builds on the concept of Fuzzy Gaussian Inference(FGI) [1][2] as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone i...

Evolutionary Optimization is becoming omnipresent technique in almost every process of intelligent system design. Just to name few, engineering, control, economics and forecasting are some of the scientific fields that take advantage of an evolutionary computational process that aid in engineering systems with intelligent behavior. This special issue of Journal of Universal Computer Science is ...

Nowadays, one of the most important areas of application of fuzzy set theory are fuzzy rule-based systems. These kinds of systems constitute an extension of classical rule-based systems, because they deal with “IF-THEN” rules whose antecedents and consequents are composed of fuzzy logic statements instead of classical logic. They have been successfully applied to a wide range of problems in dif...

An Evolutionary Algorithm (EA) strategy for the optimization of generic Work-In-Process (WIP) scheduling fuzzy controllers is presented. The EA is used to tune a set of fuzzy control modules which are used for distributed and supervisory WIP scheduling. The distributed controllers objective is to control the rate in each production stage so that satisfies the demand for final products while red...

Problems in construction management are complex, full of uncertainty, and vary with environment. Fuzzy logic (FL), neural networks (NNs), and genetic algorithms (GAs) have been successfully applied in construction management to solve various kinds of problems. Considering the characteristics and merits of each method, this paper combines the above three techniques to develop an Evolutionary Fuz...