Designing of rule base for a TSK- fuzzy system using bacterial foraging optimization algorithm (BFOA)

AbstractManual construction of a rule base for a fuzzy system is a hard and time-consuming task that requires expert knowledge. To ameliorate that, researchers have developed some methods that are more based on training data than on expert knowledge to gradually identify the structure of rule bases. In this paper we propose a method based on bacterial foraging optimization algorithm (BFOA), which simulates the foraging behavior of “E.coli” bacterium, to tune Gaussian membership functions parameters of a TSK-fuzzy system rule base. The effectiveness of modified BFOA in such identifications is then revealed for designing a fuzzy control system, via a comparison with available methods

Model Reference Adaptive Control based on a Simplified Recurrent Neural Network Trained by Gbest-Guided Gravitational Search Algorithm to Control Nonlinear Systems

This paper presents an intelligent Model Reference Adaptive Control (MRAC) strategy based on a Simplified Recurrent Neural Network (SRNN) for nonlinear dynamical systems. This network is an enhanced version of a previously reported modified recurrent network (MRN). More precisely, the enhancement in the SRNN structure was realized by employing unity weight values between the context and the hidden layers in the original MRN structure. The newly developed Gbest-guided Gravitational Search Algorithm (GGSA) was adopted for optimizing the parameters of the SRNN structure. To show the efficiency of the proposed SRNN-based MRAC, three different nonlinear systems were considered as case studies, including complex difference equations and the water bath temperature control system. From an extensive set of evaluation tests, which includes a control performance test, a disturbance rejection test, and a generalization test, the proposed SRNN-based MRAC system demonstrated its effectiveness with regards to precise control and good robustness and generalization abilities. Furthermore, compared to other Neural Network (NN) structures, including the original MRN and the Multilayer Perceptron (MLP) NN, the SRNN structure attained superior results due to the utilization of a reduced set of parameters. From another study, the GGSA accomplished the best optimization results in terms of control precision and convergence speed compared to the original Gravitational Search Algorithm (GSA)

Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System

This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations

Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm

This paper proposes zero-order Takagi-Sugeno-Kang (TSK)-type fuzzy system learning using a two-phase swarm intelligence algorithm (TPSIA). The first phase of TPSIA learns fuzzy system structure and parameters by on-line clustering-aided ant colony optimization (ACO). Phase two aims to further optimize all of the free parameters in the fuzzy system using particle swarm optimization (PSO). In clustering-aided ACO (CACO). fuzzy System Structure is learned through on-line Clustering. Once a new rule is generated by clustering. the consequent is selected from a discrete set of candidate values by ACO. In ACO. the path of an ant is regarded as a combination of consequent values selected from every rule. CACO helps to locate good initial fuzzy systems for subsequent phase learning. In Phase two, initial particles in PSO are randomly generated according to the best solution found by CACO. All free parameters in the designed fuzzy system are optimally tuned by PSO. Simulations on fuzzy control of three nonlinear plants are conducted to verify TPSIA performance. Comparisons with other learning algorithms demonstrate TPSIA performance. (C) 2008 Elsevier B.V. All rights reserved