LEADER 05717nam 22007573u 450 001 9911007277703321 005 20230803195245.0 010 $a0-486-13772-4 010 $a1-62870-072-6 035 $a(CKB)2670000000525512 035 $a(EBL)1894528 035 $a(SSID)ssj0001082436 035 $a(PQKBManifestationID)12450737 035 $a(PQKBTitleCode)TC0001082436 035 $a(PQKBWorkID)11100779 035 $a(PQKB)10227752 035 $a(MiAaPQ)EBC1894528 035 $a(Au-PeEL)EBL1894528 035 $a(CaONFJC)MIL618864 035 $a(OCoLC)765641472 035 $a(EXLCZ)992670000000525512 100 $a20141222d2014|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAdaptive Filtering Prediction and Control 205 $a1st ed. 210 $aNewburyport $cDover Publications$d2014 215 $a1 online resource (1123 p.) 225 1 $aDover Books on Electrical Engineering 300 $aDescription based upon print version of record. 311 08$a0-486-46932-8 327 $aCover; Title Page; Copyright Page; Table of Contents; Preface; 1 Introduction To Adaptive Techniques; 1.1 Filtering; 1.2 Prediction; 1.3 Control; Part I: Deterministic Systems; 2 Models for Deterministic Dynamical Systems; 2.1 Introduction; 2.2 State-Space Models; 2.2.1 General; 2.2.2 Controllable State-Space Models; 2.2.3 Observable State-Space Models; 2.2.4 Minimal State-Space Models; 2.3 Difference Operator Representations; 2.3.1 General; 2.3.2 Right Difference Operator Representations; 2.3.3 Left Difference Operator Representations; 2.3.4 Deterministic Autoregressive Moving-Average Models 327 $a2.3.5 Irreducible Difference Operator Representations2.4 Models for Bilinear Systems; 3 Parameter Estimation for Deterministic Systems; 3.1 Introduction; 3.2 On-Line Estimation Schemes; 3.3 Equation Error Methods for Deterministic Systems; 3.4 Parameter Convergence; 3.4.1 The Orthogonalized Projection Algorithm; 3.4.2 The Least-Squares Algorithm; 3.4.3 The Projection Algorithm; 3.4.4 Persistent Excitation; 3.5 Output Error Methods; 3.6 Parameter Estimation with Bounded Noise; 3.7 Constrained Parameter Estimation; 3.8 Parameter Estimation for Multi-output Systems; 3.9 Concluding Remarks 327 $a4 Deterministic Adaptive Prediction4.1 Introduction; 4.2 Predictor Structures; 4.2.1 Prediction with Known Models; 4.2.2 Restricted Complexity Predictors; 4.3 Adaptive Prediction; 4.3.1 Direct Adaptive Prediction; 4.3.2 Indirect Adaptive Prediction; 4.4 Concluding Remarks; 5 Control of Linear Deterministic Systems; 5.1 Introduction; 5.2 Minimum Prediction Error Controllers; 5.2.1 One-Step-Ahead Control (The SISO Case); 5.2.2 Model Reference Control (The SISO Case); 5.2.3 One-Step-Ahead Design for Multi-input Multi-output Systems; 5.2.4 Robustness Considerations 327 $a5.3 Closed-Loop Pole Assignment5.3.1 Introduction; 5.3.2 The Pole Assignment Algorithm (Difference Operator Formulation); 5.3.3 Rapprochement with State- Variable Feedback; 5.3.4 Rapprochement with Minimum Prediction Error Control; 5.3.5 The Internal Model Principle; 5.3.6 Some Design Considerations; 5.4 An Illustrative Example; 6 Adaptive Control Of Linear Deterministic Systems; 6.1 Introduction; 6.2 The Key Technical Lemma; 6.3 Minimum Prediction Error Adaptive Controllers (Direct Approach); 6.3.1 One-Step-Ahead Adaptive Control (The SISO Case); 6.3.2 Model Reference Adaptive Control 327 $a6.3.3 One-Step-Ahead Adaptive Controllers for Multi-input Multi-output Systems6.4 Minimum Prediction Error Adaptive Controllers (Indirect Approach); 6.5 Adaptive Algorithms for Closed-Loop Pole Assignment; 6.6 Adaptive Control of Nonlinear Systems; 6.7 Adaptive Control of Time-Varying Systems; 6.8 Some Implementation Considerations; Part II: Stochastic Systems; 7 Optimal Filtering and Prediction; 7.1 Introduction; 7.2 Stochastic State-Space Models; 7.3 Linear Optimal Filtering and Prediction; 7.3.1 The Kalman Filter; 7.3.2 Fixed-Lag Smoothing; 7.3.3 Fixed-Point Smoothing 327 $a7.3.4 Optimal Prediction 330 $aThis unified survey of the theory of adaptive filtering, prediction, and control focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems. In keeping with the importance of computers to practical applications, the authors emphasize discrete-time systems. Their approach summarizes the theoretical and practical aspects of a large class of adaptive algorithms.Ideal for advanced undergraduate and graduate classes, this treatment consists of two parts. The first section concerns deterministic systems, covering models, parameter estimation, and adaptive predic 410 0$aDover Books on Electrical Engineering 606 $aDiscrete-time systems 606 $aFilters (Mathematics) 606 $aPrediction theory 606 $aControl theory 606 $aCivil & Environmental Engineering$2HILCC 606 $aEngineering & Applied Sciences$2HILCC 606 $aOperations Research$2HILCC 615 0$aDiscrete-time systems. 615 0$aFilters (Mathematics) 615 0$aPrediction theory. 615 0$aControl theory. 615 7$aCivil & Environmental Engineering 615 7$aEngineering & Applied Sciences 615 7$aOperations Research 676 $a003/.83 700 $aGoodwin$b Graham C$g(Graham Clifford),$f1945-$013807 701 $aSin$b Kwai Sang$01825188 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9911007277703321 996 $aAdaptive Filtering Prediction and Control$94392703 997 $aUNINA