Kim, Dong Hwa,
Tuning Innovation with Biotechnology - First edition. - 1 online resource
chapter 1 Background -- chapter 2 Artificial Immune Algorithm–Based Intelligent Parameter Estimation -- chapter 3 Intelligent System Tuning Using a Hybrid GA-PSO Approach -- chapter 4 Intelligent Vector Control Using a Hybrid GA-PSO System -- chapter 5 Intelligent Tuning Using Hybrid System of GA and BF -- chapter 6 Artificial Intelligence, Emotion Function, and ICT -- chapter 7 Hybrid System by AINFS and AINFNNS for Robust Control of Nonlinear System.
"This book deals with evolving intelligence systems and their use in immune algorithm (IM), particle swarm optimization (PSO), bacterial foraging (BF), and hybrid intelligent system to improve plants, robots, etc. It discusses the motivation behind research on and background of evolving intelligence systems and illustrates IM-based approach for parameter estimation required for designing an intelligent system. It approaches optimal intelligent tuning using a hybrid genetic algorithm"particle swarm optimization (GA-PSO) and illustrates hybrid GA-PSO for intelligent tuning of vector system. "--Provided by publisher.
9781315364582 9781315321943
10.1201/9781315364582 doi
Biotechnology.
Biopharmaceutics
Nanoscience & Nanotechnology
NANOnetBASE
PHARMACEUTICALnetBASE
BIOSCIENCEnetBASE
SCI-TECHnetBASE
BIOMEDICALSCIENCEnetBASE
STMnetBASE
TP248.2 / K563 2016
660.6
Tuning Innovation with Biotechnology - First edition. - 1 online resource
chapter 1 Background -- chapter 2 Artificial Immune Algorithm–Based Intelligent Parameter Estimation -- chapter 3 Intelligent System Tuning Using a Hybrid GA-PSO Approach -- chapter 4 Intelligent Vector Control Using a Hybrid GA-PSO System -- chapter 5 Intelligent Tuning Using Hybrid System of GA and BF -- chapter 6 Artificial Intelligence, Emotion Function, and ICT -- chapter 7 Hybrid System by AINFS and AINFNNS for Robust Control of Nonlinear System.
"This book deals with evolving intelligence systems and their use in immune algorithm (IM), particle swarm optimization (PSO), bacterial foraging (BF), and hybrid intelligent system to improve plants, robots, etc. It discusses the motivation behind research on and background of evolving intelligence systems and illustrates IM-based approach for parameter estimation required for designing an intelligent system. It approaches optimal intelligent tuning using a hybrid genetic algorithm"particle swarm optimization (GA-PSO) and illustrates hybrid GA-PSO for intelligent tuning of vector system. "--Provided by publisher.
9781315364582 9781315321943
10.1201/9781315364582 doi
Biotechnology.
Biopharmaceutics
Nanoscience & Nanotechnology
NANOnetBASE
PHARMACEUTICALnetBASE
BIOSCIENCEnetBASE
SCI-TECHnetBASE
BIOMEDICALSCIENCEnetBASE
STMnetBASE
TP248.2 / K563 2016
660.6