Large-Scale Systems with Fuzzy Neural Networks

Large-Scale Systems with Fuzzy Neural Networks

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12568-2 How Large is Large? One definition of large-scale systems is that they are capable of partitioning into subsystems for computational or practical reasons. Another viewpoint defines any system as "large" if conventional techniques of modeling, analysis, and control fail to yield reasonable solutions with reasonable computational efforts. These definitions can be applied to a variety of systems in fields as diverse as business management, the environment, data networks, aerospace, transportation, and energy. All these large-scale systems require special approaches to modeling and control, and recent advances in soft computing provide new opportunities to use intelligent control to manage their complexity. Fuzzy logic, neural networks, and genetic (evolutionary) algorithms all offer new avenues for modeling, stabilization, control, and optimization of large-scale systems. This book presents past and present trends, and looks into the future potential with a special focus on computer-assisted problem-solving. Concepts are presented with their proofs, followed by an algorithm showing how to use the results. Problems are included at the end of every chapter. Major topics include: * Modeling and model reduction: aggregation, perturbation, balance aggregation, and system identification * Structural properties: stability, controllability, and observability * Control systems: hierarchical control, decentralized control, and optimum control theory * Fuzzy logic: fuzzy set theory, fuzzy mathematics, fuzzy operations research, and fuzzy control systems * This book offers an innovative yet practical approach to the challenges of large-scale systems. Software for solving the problems in the text can be ordered using the postcards included in this more

Product details

  • Hardback | 600 pages
  • 187.96 x 256.54 x 27.94mm | 975.22g
  • Pearson Education Limited
  • Prentice-Hall
  • Harlow, United Kingdom
  • English
  • 0131256831
  • 9780131256835

Table of contents

Preface. 1. Introduction to Large-Scale Systems. Historical Background. Hierarchical Structures. Decentralized Control. Artificial Intelligence. Neural Networks. Fuzzy Logic. Computer-Aided Approach. Scope. Problems. 2. Large-Scale Systems Modeling. Introduction. Aggregation Methods. General Aggregation. Modal Aggregation. Balanced Aggregation. Perturbation Methods. Weakly Coupled Models. Strongly Coupled Models. Modeling via System Identification. Problem Definition. System ID Toolbox. Modeling via Fuzzy Logic. Problems. 3. Structural Properties of Large Scale Systems. Introduction. Lyapunov Stability Methods. Definitions and Problem Statement. Stability Criteria. Connective Stability. Input-Output Stability Methods. Problem Development and Statement. IO Stability Criterion. Controllability and Observability of Composite Systems via Connectivity Approach. Preliminary Definitions. Controllability and Observability Conditions. Structural Controllability and Observability. Structure and Rank of a Matrix. Conditions for Structural Controllability. Structural Controllability and Observability via System Connectability. Computer-Aided Structural Analysis. Standard State-Space Forms. CAD Examples. Discussion and Conclusions. Discussion of the Stability of Large-Scale Systems. Discussion of the Controllability and Observability of Large-Scale Systems. Problems. 4. Hierarchical Control of Large-Scale Systems. Introduction. Coordination of Hierarchical Structures. Model Coordination Method. Goal Coordination Method. Hierarchical Control of Linear Systems. Linear System Two-level Coordination. Interaction Prediction Method. Goal Coordination and Singularities. Closed-Loop Hierarchical Control of Continuous-Time Systems. Series Expansion Approach of Hierarchical Control. Problem Formulation. Performance Index Approximation. Optimal Control. Coorinator Problem. Computer-Aided Hierarchical Control Design Examples. Problems. 5. Decentralized Control of Large-Scale Systems. Introduction. Decentralized Stabilization. Fixed Polynomials and Fixed Modes. Stabilization via Dynamic Compensation. Stabilization via Multilevel Control. Exponential Stabilization. Decentralized Adaptive Control. Decentralized Adaptation. Decentralized Regulation Systems. Decentralized Tracking Systems. Liquid-Metal Cooled Reactor. Application of Model Reference Adaptive Control. Discussion and Conclusions. Problems. 6. Near-Optimum Design of Large-Scale Systems. Introduction. Near-Optimum Control of Linear Time-Invariant Systems. Aggregation Methods. Perturbation Methods. Decentralized Control via Unconstrained Minimization. Near-Optimum Control of Large-Scale Nonlinear Systems. Near-Optimum Control via Sensitivity Methods. Hierarchical Control via Interaction Prediction. Bounds on Near-Optimum Cost Functional. Near-Optimality Due to Aggregation. Near-Optimality Due to Perturbation. Near-Optimality in Hierarchical Control. Near-Optimality in Nonlinear Systems. Computer-Aided Design. Problems. 7. Fuzzy Control Systems-Structures and Stability. Introduction. Fuzzy Control Structures. Basic Definitions and Architectures. Fuzzification. Inference Engine. Defuzzification Methods. The Inverted Pendulum Problem. Overshoot-Suppressing Fuzzy Controllers. Analysis of Fuzzy Control System. Stability of Fuzzy Control Systems. Introduction. Fuzzy Control Systems' Stability Classes. Lyapunov Stability of Fuzzy Control Systems. Fuzzy System Stability via Interval Matrix. Method. Problems. 8. Fuzzy Control Systems-Adaptation and Hierarchy. Introduction. Adaptive Fuzzy Control Systems. Adaptation by Parameter Estimation. Adaptive Fuzzy Multiterm Controllers. Indirect Adaptive Fuzzy Control. Large-Scale Fuzzy Control Systems. Hierarchical Fuzzy Control. Rule-Base Reduction. Hybrid Control Systems. Problems. Appendix A. Brief Review of Fuzzy Set Theory. Introduction. Fuzzy Sets versus Crisp Sets. The Shape of Fuzzy Sets. Fuzzy Sets Operations. Fuzzy Logic and Approximate Reasoning. Problems. Apprendix B. The Fuzzy Logic Development Kit. Introduction. Description of the FULDEK Program. EDITOR Option. The RUN Option. Post-Processing Feature of FULDEK. A Real-Time Laser Beam Fuzzy Controller. New Options in Version 4.0 of the FULDEK Program. Conclusion. References. more

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