Recent Advances in Interval Type-2 Fuzzy Systems by Oscar Castillo

By Oscar Castillo

This ebook stories present state-of-the-art equipment for development clever platforms utilizing type-2 fuzzy common sense and bio-inspired optimization suggestions. Combining type-2 fuzzy good judgment with optimization algorithms, robust hybrid clever platforms were equipped utilizing the benefits that every method bargains. This e-book is meant to be a reference for scientists and engineers attracted to utilising type-2 fuzzy common sense for fixing difficulties in trend acceptance, clever keep an eye on, clever production, robotics and automation. This ebook can be used as a reference for graduate classes just like the following: tender computing, clever trend reputation, laptop imaginative and prescient, utilized man made intelligence, and related ones. We think about that this publication is usually used to get novel rules for brand new strains of re-search, or to proceed the traces of analysis proposed by means of the authors.

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Type-1 fuzzy sets used in conventional fuzzy systems cannot fully handle the uncertainties present but type-2 fuzzy sets that are used in type-2 fuzzy systems can handle such uncertainties in a better way because they provide more parameters and more design degrees of freedom. There are membership functions which can be parameterized by a few variables and when optimized, the membership optimization problem can be reduced to a parameter optimization problem. This work deals with the parameter optimization of the type-2 fuzzy membership functions using a new proposed reinforcement learning algorithm in a nonlinear system.

Li, H. Liu, D. Brown, Adaptive fuzzy controller for vehicle active suspensions with particle swarm optimization, in Proceedings of SPIE—The International Society of Optical Engineering, 2008, p. 7129 4. -S. -S. -K. Oh, The design of optimized fuzzy neural networks and its application. Trans. Korean Inst. Electr. Eng. 58(6), 1615–1623 (2009) 5. R. Martinez, A. Rodriguez, O. T. Aguilar, Type-2 fuzzy logic controllers optimization using genetic algorithms and particle swarm optimization, in Proceedings of the IEEE International Conference on Granular Computing, GrC 2010, 2010, pp.

The obtained simulation results were statistically compared with the obtained previous work results achieved with GAs in order to determine the best optimization technique for this particular robotics problem. Both PSO and ACO were able to outperform GAs for this particular application. However, in comparing ACO and PSO, the best results were achieved with ACO. In this case, the authors claim that ACO is best suited for this particular robotic problem. In the work of Juang and Hsu [5], a new reinforcement-learning method using Online Rule Generation and Q-value-aided Ant Colony Optimization (ORGQACO) for fuzzy controller design was proposed.

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