LEADER 04254nam  22008175  450 
001 9910299488103321
005 20200630151844.0
010   $a3-319-05245-4
024 7 $a10.1007/978-3-319-05245-8
035   $a(CKB)3710000000093968
035   $a(EBL)1698351
035   $a(OCoLC)880449552
035   $a(SSID)ssj0001186848
035   $a(PQKBManifestationID)11787418
035   $a(PQKBTitleCode)TC0001186848
035   $a(PQKBWorkID)11240698
035   $a(PQKB)10054039
035   $a(MiAaPQ)EBC1698351
035   $a(DE-He213)978-3-319-05245-8
035   $a(PPN)177822937
035   $a(EXLCZ)993710000000093968
100   $a20140313d2014      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aChemical Optimization Algorithm for Fuzzy Controller Design /$fby Leslie Astudillo, Patricia Melin, Oscar Castillo
205   $a1st ed. 2014.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014.
215   $a1 online resource (81 p.)
225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3704
300   $aDescription based upon print version of record.
311   $a3-319-05244-6 
320   $aIncludes bibliographical references at the end of each chapters and index.
327   $aIntroduction -- Theory and Background -- Chemical Definitions -- The Proposed Chemical Reaction Algorithm -- Application Problems -- Simulation Results -- Conclusions.
330   $aIn this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application.
410  0$aSpringerBriefs in Computational Intelligence,$x2625-3704
606   $aComputational intelligence
606   $aControl engineering
606   $aChemistry, Physical and theoretical
606   $aRobotics
606   $aAutomation
606   $aArtificial intelligence
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010
606   $aTheoretical and Computational Chemistry$3https://scigraph.springernature.com/ontologies/product-market-codes/C25007
606   $aRobotics and Automation$3https://scigraph.springernature.com/ontologies/product-market-codes/T19020
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
615  0$aComputational intelligence.
615  0$aControl engineering.
615  0$aChemistry, Physical and theoretical.
615  0$aRobotics.
615  0$aAutomation.
615  0$aArtificial intelligence.
615 14$aComputational Intelligence.
615 24$aControl and Systems Theory.
615 24$aTheoretical and Computational Chemistry.
615 24$aRobotics and Automation.
615 24$aArtificial Intelligence.
676   $a006.3
700   $aAstudillo$b Leslie$4aut$4http://id.loc.gov/vocabulary/relators/aut$0866746
702   $aMelin$b Patricia$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aCastillo$b Oscar$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910299488103321
996   $aChemical Optimization Algorithm for Fuzzy Controller Design$91934740
997   $aUNINA