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Tuning Innovation with Biotechnology

By: Kim, Dong Hwa [author.]Material type: Text

TextPublisher: Pan Stanford Publishing, 2016Edition: First editionDescription: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781315364582; 9781315321943Subject(s): Biotechnology | Biopharmaceutics | Nanoscience & Nanotechnology | NANOnetBASE | PHARMACEUTICALnetBASE | BIOSCIENCEnetBASE | SCI-TECHnetBASE | BIOMEDICALSCIENCEnetBASE | STMnetBASEAdditional physical formats: Print version: : No titleDDC classification: 660.6 LOC classification: TP248.2 | K563 2016Online resources: Click here to view.

Contents:

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.

Scope and content: "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.

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"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.