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Performance Analysis and Synthesis for Discrete-Time Stochastic Systems with Network-Enhanced Complexities
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Performance Analysis and Synthesis for Discrete-Time Stochastic Systems with Network-Enhanced Complexities

Book Details

Format Hardback or Cased Book
ISBN-10 1138610011
ISBN-13 9781138610019
Publisher Taylor & Francis Ltd
Imprint CRC Press
Country of Manufacture GB
Country of Publication GB
Publication Date Oct 8th, 2018
Print length 268 Pages
Weight 564 grams
Dimensions 16.20 x 24.20 x 2.30 cms
Ksh 35,100.00
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This book aims to provide a unified treatment on the analysis and synthesis for discrete-time stochastic systems with guarantee of certain performances against network-enhanced complexities with applications in sensor networks and mobile robotics.

The book addresses the system performance with a focus on the network-enhanced complexities and developing the engineering-oriented design framework of controllers and filters with potential applications in system sciences, control engineering and signal processing areas. Therefore, it provides a unified treatment on the analysis and synthesis for discrete-time stochastic systems with guarantee of certain performances against network-enhanced complexities with applications in sensor networks and mobile robotics. Such a result will be of great importance in the development of novel control and filtering theories including industrial impact.

Key Features

  • Provides original methodologies and emerging concepts to deal with latest issues in the control and filtering with an emphasis on a variety of network-enhanced complexities
  • Gives results of stochastic control and filtering distributed control and filtering, and security control of complex networked systems
  • Captures the essence of performance analysis and synthesis for stochastic control and filtering
  • Concepts and performance indexes proposed reflect the requirements of engineering practice
  • Methodologies developed in this book include backward recursive Riccati difference equation approach and the discrete-time version of input-to-state stability in probability

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