Titlebook: Recent Advances on Hybrid Approaches for Designing Intelligent Systems; Oscar Castillo,Patricia Melin,Janusz Kacprzyk Book 2014 Springer I

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Genetic Algorithm Optimization for Type-2 Non-singleton Fuzzy Logic Controllers non-singleton fuzzy logic controller. A genetic algorithm is used to obtain an automatic design of the type-2 non-singleton fuzzy logic controller (NSFLC). Simulation results are obtained with Simulink showing the behavior of the mobile robot whit this type of controller.

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Optimization of Fuzzy Control Systems for Mobile Robots Based on PSOr block control that combines both fuzzy inference systems (FIS). The integrator block is called Weighted Fuzzy Inference System (WFIS) and assigns weights to the responses in each block of behavior in order to combine them into a single response. A comparison with the results obtained with genetic algorithms is also performed.

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Book 2014nt control and robotics, pattern recognition, time series prediction and optimization complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks and bio-inspired optimization algorithms, which can be used to produce powerful hybr

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Genetic Optimization of Type-2 Fuzzy Integrators in Ensembles of ANFIS Models for Time Series Predication of memberships function parameters of each interval type-2 fuzzy integrators. In the experiments we optimized Gaussians, Generalized Bell and Triangular membership functions for each of the fuzzy integrators, thereby increasing the complexity of the training. Simulation results show the effectiveness of the proposed approach.

TOXIC

Optimization of Fuzzy Controllers Design Using the Bee Colony Algorithmterms of quality and consistency in the results, even if the method is one of the latest in the field of collective intelligence. Similarly presents some interesting observations derived from observed performance.

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for functionally and territorially defined areas between the county and the central level of government without any political decision-making bodies, for example, the four functionally defined health regions, the territorially defined areas called . (NUTS 2), and the 89 economic regions (NUTS 4). I

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Ricardo Martínez-Soto,Oscar Castillo,Juan R. Castro for functionally and territorially defined areas between the county and the central level of government without any political decision-making bodies, for example, the four functionally defined health regions, the territorially defined areas called . (NUTS 2), and the 89 economic regions (NUTS 4). I