Soft Computing for Intelligent Control and Mobile Robotics (Studies in Computational Intelligence)

Soft Computing for Intelligent Control and Mobile Robotics (Studies in Computational Intelligence)

Language: English

Pages: 484

ISBN: 3642155332

Format: PDF / Kindle (mobi) / ePub

This book describes in a detailed fashion the application of hybrid intelligent systems using soft computing techniques for intelligent control and mobile robotics. 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 hybrid intelligent systems. The prudent combination of SC techniques can produce powerful hybrid intelligent systems that are capable of solving real-world problems. This is illustrated in this book with a wide range of applications, with particular emphasis in intelligent control and mobile robotics. The book is organized in five main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theory and algorithms, which are basically papers that propose new models and concepts, which can be the basis for achieving intelligent control and mobile robotics. The second part contains papers with the main theme of intelligent control, which are basically papers using bio-inspired techniques, like evolutionary algorithms and neural networks, for achieving intelligent control of non-linear plants. The third part contains papers with the theme of optimization of fuzzy controllers, which basically consider the application of bio-inspired optimization methods to automate the de-sign process of optimal type-1 and type-2 fuzzy controllers. The fourth part contains papers that deal with the application of SC techniques in times series prediction and intelligent agents. The fifth part contains papers with the theme of computer vision and robotics, which are papers considering soft computing methods for applications related to vision and robotics.

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32 64 128 TIME IN MINUTES Core1 1.01 1.30 1.58 3.30 Core2 1.01 1.31 1.59 3.31 Core3 1.02 1.23 2.00 3.32 Core4 1.05 1.26 2.01 3.30 In table 9, we can appreciate the simulation results in parallel mode for the Griewank’s function. In Table 10, we show the time of execution of the method for this test function. Table 9. Experimental results with FPSO+FGA for the Griewank’s function V 16 32 64 128 BEST VALUE AVERAGE 0.0345 (Core1) 0.1045 (Core1) 0.9981 (Core1) 1.5567 (Core1) WORST VALUE

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