Soft Computing for Control of Non-Linear Dynamical Systems

Studies in Computational Intelligence

2014

Self Cite

In this chapter a new method of hierarchical genetic algorithm for fuzzy inference systems optimization is proposed. This method was used in two applications, the first was to perform the combination of responses of modular neural networks for human recognition based on face, iris, ear and voice, and the second one for fuzzy control of temperature in the shower benchmark problem. The results obtained by non-optimized type-2 fuzzy inference system can be improved using the proposed hierarchical genetic algorithm as can be verified by the simulations.

Section: Type-2 Fuzzy Logicmentioning

confidence: 99%

Hierarchical Genetic Algorithms for Type-2 Fuzzy System Optimization Applied to Pattern Recognition and Fuzzy Control

Sánchez

Studies in Computational Intelligence

2014

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“…The basic principles of GAs were first proposed by John Holland in 1975, inspired by the mechanism of natural selection, where stronger individuals are likely the winners in a competing environment [24]. GAs assumes that the potential solution of any problems an individual and can represented by a set of parameters [33].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Optimization of the Interval Type-2 Fuzzy Integrators in Ensembles of ANFIS Models for Time Series Prediction: Case of the Mexican Stock Exchange

Soto

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

2015

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This paper describes the optimization of the fuzzy integrators in Ensembles of ANFIS model for time series prediction: case of the Mexican Stock Exchange. The Mexican stock exchange that is used corresponds to the period of 11/09/2005 to 01/15/2009 to simulate the performance of the proposed architecture. We used interval type-2 fuzzy systems to integrate the outputs (forecast) of each of the ANFIS models in the Ensemble. Genetic Algorithms (GAs) are used for the optimization of memberships function "MFs" for the 2 MFs (used linguistic labels "Small and Large") and for the 3 MFs (used linguistic labels "Small, Middle and Large") parameters of the fuzzy integrators. In the experiments the genetic algorithms optimized the Gaussian, Generalized Bell and Triangular membership functions for each of the fuzzy integrators; in the interval type-2 fuzzy integrator there are more parameters, thereby increasing the complexity of the training for the fuzzy integrators. Simulation results show the effectiveness of the proposed approach in comparison with other researchers.

“…Nevertheless, the main problem with fuzzy logic is that there is no systematic procedure for the design of a fuzzy controller. For this reason, we propose in this paper the use of the adaptive neuro-fuzzy inference system (ANFIS) methodology [7,8] to adapt the parameters of the fuzzy system for control [2,4]. We use this neuro-fuzzy approach to train the controller with real data about the problem.…”

Section: Introductionmentioning

confidence: 98%

Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy approach

Castillo

2003

Soft Computing

Stepping motors are widely used in robotics and in the numerical control of machine tools where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and a closed-loop control becomes necessary. In this paper, fuzzy logic is applied to control the speed of a stepping motor drive with feedback. A neuro-fuzzy hybrid approach is used to design the fuzzy rule base of the intelligent system for control. In particular, we used the ANFIS methodology to build a Sugeno fuzzy model for controlling the stepping motor drive. An advanced test bed is used in order to evaluate the tracking properties and the robustness capacities of the fuzzy logic controller.