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140407 ||| eng |
| 020 |
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|a 9783319052458
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| 100 |
1 |
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|a Astudillo, Leslie
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| 245 |
0 |
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|a Chemical Optimization Algorithm for Fuzzy Controller Design
|h Elektronische Ressource
|c by Leslie Astudillo, Patricia Melin, Oscar Castillo
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| 250 |
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|a 1st ed. 2014
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2014, 2014
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| 300 |
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|a VIII, 77 p. 32 illus
|b online resource
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| 505 |
0 |
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|a Introduction -- Theory and Background -- Chemical Definitions -- The Proposed Chemical Reaction Algorithm -- Application Problems -- Simulation Results -- Conclusions
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| 653 |
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|a Chemistry, Physical and theoretical
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| 653 |
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|a Control, Robotics, Automation
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| 653 |
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|a Theoretical Chemistry
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Control and Systems Theory
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Control engineering
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Robotics
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| 653 |
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|a Automation
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| 700 |
1 |
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|a Melin, Patricia
|e [author]
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| 700 |
1 |
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|a Castillo, Oscar
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a SpringerBriefs in Computational Intelligence
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| 028 |
5 |
0 |
|a 10.1007/978-3-319-05245-8
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-05245-8?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.3
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| 520 |
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|a 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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