05376nam 2200673 450 991013901550332120200903223051.01-118-79651-91-118-79635-71-118-79648-9(CKB)2550000001115801(EBL)1376955(OCoLC)861529063(SSID)ssj0001036150(PQKBManifestationID)11992646(PQKBTitleCode)TC0001036150(PQKBWorkID)11050820(PQKB)10406502(MiAaPQ)EBC1376955(Au-PeEL)EBL1376955(CaPaEBR)ebr10756812(CaONFJC)MIL516136(PPN)190065273(EXLCZ)99255000000111580120130612d2013 uy| 0engur|n|---|||||txtccrMetaheuristic optimization for the design of automatic control laws /Guillaume SandouHoboken, NJ :ISTE Ltd/John Wiley and Sons Inc,2013.1 online resource (140 p.)Focus automation and control series,2051-2481Description based upon print version of record.1-84821-590-8 1-299-84885-0 Includes bibliographical references and index.""Cover ""; ""Title Page ""; ""Contents ""; ""Preface ""; ""Chapter 1. Introduction And Motivations ""; ""1.1. Introduction: automatic control and optimization ""; ""1.2. Motivations to use metaheuristic algorithms ""; ""1.3. Organization of the book ""; ""Chapter 2. Symbolic Regression """"2.1. Identification problematic and brief state of the art """"2.2. Problem statement and modeling ""; ""2.2.1. Problem statement ""; ""2.2.2. Problem modeling ""; ""2.3. Ant colony optimization ""; ""2.3.1. Ant colony social behavior ""; ""2.3.2. Ant colony optimization """"2.3.3. Ant colony for the identification of nonlinear functions with unknown structure """"2.4. Numerical results ""; ""2.4.1. Parameter settings ""; ""2.4.2. Experimental results ""; ""2.5. Discussion ""; ""2.5.1. Considering real variables ""; ""2.5.2. Local minima """"2.5.3. Identification of nonlinear dynamical systems """"2.6. A note on genetic algorithms for symbolic regression ""; ""2.7. Conclusions ""; ""Chapter 3. Pid Design Using Particle Swarm Optimization ""; ""3.1. Introduction ""; ""3.2. Controller tuning: a hard optimization problem """"3.2.1. Problem framework """"3.2.2. Expressions of time domain specifications ""; ""3.2.3. Expressions of frequency domain specifications ""; ""3.2.4. Analysis of the optimization problem ""; ""3.3. Particle swarm optimization implementation ""; ""3.4. PID tuning optimization """"3.4.1. Case study: magnetic levitation ""The classic approach in Automatic Control relies on the use of simplified models of the systems and reformulations of the specifications. In this framework, the control law can be computed using deterministic algorithms. However, this approach fails when the system is too complex for its model to be sufficiently simplified, when the designer has many constraints to take into account, or when the goal is not only to design a control but also to optimize it. This book presents a new trend in Automatic Control with the use of metaheuristic algorithms. These kinds of algorithm can optimize any crFocus series in automation & control.Mathematical optimizationHeuristic algorithmsMathematical optimization.Heuristic algorithms.519.6Sandou Guillaume977732MiAaPQMiAaPQMiAaPQBOOK9910139015503321Metaheuristic optimization for the design of automatic control laws2227458UNINA01142nam 2200397 450 991015313400332120230617023738.01-118-69504-61-118-69494-5(CKB)3710000000960193(MiAaPQ)EBC4747117(EXLCZ)99371000000096019320161207h20051996 uy 0engurcnu||||||||rdacontentrdamediardacarrierFeeding and care of the horse /Lon D. LewisSecond edition.Oxford, England ;Victoria, Australia :Blackwell Publishing,2005.©19961 online resource (485 pages) illustrations (some color), map, tablesIncludes index.0-683-04967-4 HorsesFeeding and feedsHorsesFeeding and feeds.636.1084Lewis Lon D.306756MiAaPQMiAaPQMiAaPQBOOK9910153134003321Feeding and care of the horse35994UNINA00646nam 2200217zu 450 991097837100332120250222215927.0(CKB)37656823300041(EXLCZ)993765682330004120250222|2024uuuu || |engur|||||||||||Editorial Board Members' Collection SeriesMDPI - Multidisciplinary Digital Publishing Institute20249783725827060 3725827060 Salkuti Surender Reddy1294634BOOK9910978371003321Editorial Board Members' Collection Series4319944UNINA