Titlebook: Soft Computing for Hybrid Intelligent Systems; Oscar Castillo,Patricia Melin,Witold Pedrycz Book 2008 Springer-Verlag Berlin Heidelberg 20

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concentrate

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Fuzzy Control for Output Regulation of a Servomechanism with Backlashmeasurement available for feedback, the proposed method is far from trivial because of non-minimum phase properties of the system. Simulation results illustrate the effectiveness of the closed-loop system.

sinoatrial-node

Interval Type-2 Fuzzy Logic for Module Relevance Estimation in Sugeno Integration of Modular Neural put of the inference system are the fuzzy densities to calculate the fuzzy . measures. Although this integration method was tested for image recognition, is possible to adapt it for distinct applications, which need information fusion of sources with uncertain relevance.

Cognizance

A New Biometric Recognition Technique Based on Hand Geometry and Voice Using Neural Networks and Fuz, palm, wrist, also a segment of the palm; they appear to be from a with a fuzzy system that will tell us how much they seemed to a certain person, comparing each variable given by the preprocessing of the image according to the data base that its already stored (all the images of the individuals, voice, etc.).

nitric-oxide

A Hybrid Model Based on a Cellular Automata and Fuzzy Logic to Simulate the Population Dynamicsare induced by a stochastic variation of the environment. The model can be used as computational tool in the area of the Biology to describe and quantify the changes that continuously occurs in the population, knowing not only their size and its structure, but the form and the intensity in which it changes and renews.

Morbid

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Comparison between Ant Colony and Genetic Algorithms for Fuzzy System Optimizationevolutionary methods (HGA, ACO), so that we could make the comparisons between them via average and standard deviation, and concluding with the best evolutionary method for a fuzzy system optimization control problem.

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Optimization of Response Integration with Fuzzy Logic in Ensemble Neural Networks Using Genetic Algove the recognition rate of the individual modules. Using GA’s to optimize the Membership Functions of The Type-1 Fuzzy System and Type-2 Fuzzy System we can improve the results of the fuzzy systems. We show in this paper the results of a type-2 approach for response integration that improves performance over the type-1 logic approaches.