Chemical Optimization Algorithm for Fuzzy Controller Design (SpringerBriefs in Applied Sciences and Technology) de Astudillo, Leslie, Melin, Patricia, Castillo, Oscar: Very Good (2014)

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Detalles bibliogr�ficos

T�tulo: Chemical Optimization Algorithm for Fuzzy Controller Design (SpringerBriefs in Applied Sciences and Technology)
Editor: Springer
Fecha de publicaci�n: 2014
Idioma: Ingl�s
ISBN 10: 3319052446
ISBN 13: 9783319052441
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: Very Good

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Sinopsis

In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions.

This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application.

De la contraportada

In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions.

This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application

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