MARC details
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08531nam a22002537a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20220512151719.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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220512b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780198063155 |
| 028 ## - DISTRIBUTOR NAME |
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| Distributor Name |
:Donated from Prof Madan Gopal |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.3 BEH |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Behera, Laxmidhar |
| 245 ## - TITLE STATEMENT |
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| Title |
Intelligent systems and control |
| Remainder of title |
: principles and applications |
| Statement of responsibility, etc |
/ Laxmidhar Behera |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
New Delhi |
| Name of publisher, distributor, etc |
: Oxford University Press, |
| Date of publication, distribution, etc |
©2009 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiii, 373 pages |
| Other physical details |
: illustrations |
| Dimensions |
; 25 cm |
| 365 ## - TRADE PRICE |
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| Price amount |
Donated |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Contents note |
Machine generated contents note: 1. Non-linear Control: Primer --<br/>1.1. Norms of Signals, Vectors, and Matrices --<br/>1.2. Positive Definite Functions --<br/>1.3. Positive Definite Matrices --<br/>1.4. Continuous Time State-Space Model --<br/>1.4.1. LTI State-Space Model --<br/>1.5. Non-linear State-Space Model --<br/>1.5.1. Equilibrium Point and Linearization using First-order Taylor Series --<br/>1.5.2. Linearization Technique for Operating Points Other Than the Origin --<br/>1.6. Lyapunov Stability Theory --<br/>1.6.1. Lyapunov Stability of Time Invariant System --<br/>1.6.2. LaSalle's Invariance Theorem --<br/>1.6.3. Chetaev's Instability Theorem --<br/>1.6.4. Lyapunov Stability of Time Varying System --<br/>1.6.5. Lyapunov's Indirect Method --<br/>1.6.6. Lyapunov Stability for Linear Systems --<br/>1.7. Discrete Time Systems --<br/>1.7.1. Discrete Time LTI State-Space Model --<br/>1.7.2. Discrete Time Non-linear State-Space Model --<br/>1.7.3. ARMAX and NARMAX Models --<br/>1.7.4. Lyapunov Stability for Discrete Time Systems --<br/>1.8. Modelling of Different Non-linear Systems --<br/>1.8.1. Inertial Wheel Pendulum --<br/>1.8.2. Two Link Manipulator --<br/>1.8.3. An Inverted Pendulum Mounted on a Cart --<br/>1.8.4. Induction Motor --<br/>1.9. Non-linear Control Strategies --<br/>1.9.1. Feedback Linearization --<br/>1.9.2. Back-stepping Design --<br/>1.9.3. State Feedback Linearizable Systems --<br/>2. Neural Networks --<br/>2.1. Feed-forward Networks --<br/>2.2. Multi-layered Neural Networks --<br/>2.2.1. Principle of Gradient Descent --<br/>2.2.2. Derivation of Back Propagation Algorithm --<br/>2.2.3. Generalized Delta Rule --<br/>2.2.4. Convergence of the BP Learning Algorithm --<br/>2.3. Radial Basis Function Networks --<br/>2.3.1. Radial Basis Functions --<br/>2.3.2. Learning in RBFN --<br/>2.4. Adaptive Learning Rate --<br/>2.4.1. Lyapunov Function Based Adaptive Learning Rate --<br/>2.5. Feedback Networks --<br/>2.5.1. Response of Recurrent Networks --<br/>2.5.2. Learning Algorithms --<br/>2.5.3. Back Propagation Through Time --<br/>2.5.4. Real Time Recurrent Learning --<br/>2.6. Kohonen Self-organizing Map --<br/>2.7. System Identification Using Neural Networks --<br/>2.8. SOM Based Identification --<br/>3. Fuzzy Logic --<br/>3.1. Classical Sets --<br/>3.1.1. Operations on Classical Sets --<br/>3.2. Fuzzy Sets --<br/>3.2.1. Concept of a Fuzzy Number --<br/>3.2.2. Operations on Fuzzy Sets --<br/>3.2.3. Other Fuzzy Operations --<br/>3.2.4. Properties of Fuzzy Sets --<br/>3.2.5. Some Typical Membership Functions --<br/>3.2.6. Fuzzy Membership versus Probability --<br/>3.2.7. Extension Principle of Fuzzy Sets --<br/>3.2.8. Crisp Relation --<br/>3.2.9. Fuzzy Relations --<br/>3.2.10. Projection of Fuzzy Relations --<br/>3.2.11. Cylindrical Extension of Fuzzy Relations --<br/>3.2.12. Relation Inference --<br/>3.3. Fuzzy Rule Base and Approximate Reasoning --<br/>3.3.1. Fuzzy Linguistic Variables --<br/>3.3.2. Linguistic Modifier --<br/>3.3.3. Rule-base Systems --<br/>3.3.4. Fuzzy Rule Base --<br/>3.3.5. Fuzzy Implication Relations --<br/>3.3.6. Fuzzy Compositional Rules --<br/>3.3.7. Inference Mechanism Compared --<br/>3.3.8. Approximate Reasoning --<br/>3.4. Fuzzy Logic Control --<br/>3.4.1. Mamdani Model --<br/>3.4.2. Takagi-Sugeno Fuzzy Model --<br/>3.5. System Identification Using T-S Fuzzy Models --<br/>3.5.1. The T-S Model from Input-Output Data --<br/>3.5.2. The T-S Fuzzy Model Using Linearization --<br/>4. Indirect Adaptive Control Using Neural Networks --<br/>4.1. Continous Time Affine Systems --<br/>4.1.1. Model Identification --<br/>4.1.2. Controller Design --<br/>4.2. Discrete Time Affine Systems --<br/>4.2.1. Model Identification --<br/>4.2.2. Controller Design --<br/>4.3. Discrete Time Non-affine System --<br/>4.3.1. Model Identification --<br/>4.3.2. Controller Design: Traditional NN Approach --<br/>4.3.3. Controller Design: Network Inversion --<br/>Appendix --<br/>5. Direct Adaptive Control Using Neural Networks --<br/>5.1. Direct Adaptive Control --<br/>5.2. Single Input Single Output Affine Systems --<br/>5.2.1.f(x) is Unknown But g(x) is Known --<br/>5.2.2.f(x) and g(x) Both are Unknown --<br/>5.3. Multi-input Multi-output Systems --<br/>5.4. Single Input Single Output Discrete Time Affine Systems --<br/>5.4.1.f(x) is Unknown But g(x) is Known --<br/>5.4.2.f(x) and g(x) Both Are Unknown --<br/>5.5. Back-stepping Control --<br/>5.5.1. System Description --<br/>5.5.2. Traditional Back-stepping Design --<br/>5.5.3. Robust Back-stepping Controller Design Using RBFN --<br/>5.5.4. Back-stepping Control for a Robot Manipulator --<br/>6. Approximate Dynamic Programming --<br/>6.1. Linear Quadratic Regulator --<br/>6.2. The HJB Formulation --<br/>6.3. HJB for Affine Systems --<br/>6.4. HDP and DHP --<br/>6.5. Single Network Adaptive Critic --<br/>6.6. Continuous Time Adaptive Critic --<br/>6.7. Adaptive Critic Using the T-S Fuzzy Model --<br/>6.7.1. Continuous Time Adaptive Critic --<br/>6.7.2. Discrete Time Adaptive Critic --<br/>7. Fuzzy Logic Control --<br/>7.1. Construction of an FLC --<br/>7.2. Fuzzy PD Controller --<br/>7.2.1. The Rule Base --<br/>7.2.2. Membership Function --<br/>7.2.3. Fuzzy Parameter Optimization --<br/>7.2.4. Rule Generation Using Optimization Technique --<br/>7.3. Fuzzy PI Controller --<br/>7.3.1. The Rule Base for the Fuzzy PI Controller --<br/>7.3.2. Membership Function --<br/>7.3.3. Parameter Optimization and Rule Generation Using UMDA --<br/>7.4. Fuzzy PI Controller for a Series DC Motor --<br/>7.4.1. Parameter Optimization and Rule Generation --<br/>7.5. FLC Using Lyapunov Synthesis --<br/>7.5.1. Rotational-Translational Proof Mass Actuator --<br/>7.6. Horizontal Planar Two Link Robot Manipulator --<br/>7.6.1. Arm Posture --<br/>7.6.2. Elbow Control --<br/>7.6.3. Controller Design --<br/>Appendix --<br/>8. Takagi --<br/>Sugeno Fuzzy Model Based Control --<br/>8.1.T-S Fuzzy Model --<br/>8.2. Linear Matrix Inequality Technique --<br/>8.2.1.Common Lyapunov Matrix Criterion for Stability of the T-S Model --<br/>8.2.2. Parallel Distributed Fuzzy Compensator --<br/>8.3. Fixed Gain State Feedback Controller Design Technique --<br/>8.3.1. Fixed Gain State Feedback Controller --<br/>8.4. Variable Gain Controller Design Using Single Linear Nominal Plant --<br/>8.4.1. The Control Problem --<br/>8.4.2. Variable Gain Controller I --<br/>8.5. Variable Gain Controller Design Using Each Linear Subsystem as Nominal Plant --<br/>8.5.1. The Control Problem --<br/>8.5.2. Variable Gain Controller II --<br/>8.6. Controller Design Using Discrete T-S Fuzzy System --<br/>8.6.1. Linear State Feedback Controller for Discrete T-S Fuzzy System --<br/>Appendix --<br/>9. Intelligent Control of a Pendulum on a Cart --<br/>9.1.T-S Fuzzy Model Representation --<br/>9.2. Control Using the T-S Fuzzy Model --<br/>9.3.Network Inversion Based Control --<br/>9.3.1. Continuous-time Iterative Update --<br/>9.3.2. Discrete-time Update --<br/>9.4.T-S Fuzzy Controller --<br/>9.4.1. Continuous Time Weight Update Law --<br/>9.4.2. Discrete Time Weight Update Law --<br/>9.5. Cart-Pole System: Simulation and Experiment --<br/>9.5.1.T-S Fuzzy Model of the Cart-Pole --<br/>9.5.2. Control Systems Design --<br/>9.5.3. Experiment on a Cart-Pole System --<br/>10. Visual Motor Control of a Redundant Manipulator --<br/>10.1. System Model --<br/>10.1.1. Experimental Set-up --<br/>10.1.2. The Manipulator Model --<br/>10.1.3. The Camera Model --<br/>10.2. Visual Motor Control Using Neural Networks --<br/>10.2.1. Visual Motor Control with KSOM --<br/>10.2.2. Simulation and Experimental Results --<br/>10.2.3. Training --<br/>10.2.4. Testing --<br/>10.2.5. Real-time Experiment --<br/>10.3. Visual Motor Control Using a Fuzzy Network --<br/>10.3.1. Fuzzy C-Mean Clustering --<br/>10.3.2. Multi-step Incremental Learning --<br/>10.3.3. Simulation and Experimental Results --<br/>10.3.4. VMC Using Incremental Learning. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
<br/>Intelligent Systems and Control: Principles and Applications is a textbook for undergraduate students of electrical and computer science engineering as also postgraduate students undertaking courses on intelligent control, intelligent systems, adaptive control, and nonlinear control. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Computer science, |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Special computer methods (e.g. AI, multimedia, VR)[4] 007–009 [Unassigned] |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Intelligent control systems. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Expert systems (Computer science) |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Behera, Laxmidhar |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Mathematics Departmental Library |
