Oliver Nelles, "Nonlinear System Identification"

 

 Springer-Verlag, Berlin, Heidelberg, Germany, 2001!!
ISBN 3-540-67369-5

 

1. Introduction (1)
1.1. Relevance of Nonlinear System Identification (1)
1.2. Tasks in Nonlinear System Identification (6)
1.3. White Box, Black Box, and Gray Box Models (15)
1.4. Outline of the Book and Some Reading Suggestions (16)
1.5. Terminology (18)
Part I. Optimization Techniques
2. Introduction to Optimization (23)
2.1. Overview of Optimization Techniques (25)
2.2. Kangaroos (25)
2.3. Loss Functions for Supervised Methods (28)
2.4. Loss Functions for Unsupervised Methods (34)
3. Linear Optimization (35)
3.1. Least Squares (LS) (36)
3.2. Recursive Least Squares (RLS) (60)
3.3. Linear Optimization with Inequality Constraints (66)
3.4. Subset Selection (67)
3.5. Summary (77)
4. Nonlinear Local Optimization (79)
4.1. Batch and Sample Adaptation (81)
4.2. Initial Parameters (83)
4.3. Direct Search Algorithms (86)
4.4. General Gradient-Based Algorithms (90)
4.5. Nonlinear Least Squares Problems (102)
4.6. Constrained Nonlinear Optimization (107)
4.7. Summary (110)
5. Nonlinear Global Optimization (113)
5.1.Simulated Annealing (SA) (116)
5.2. Evolutionary Algorithms (EA) (120)
5.3. Branch and Bound (B&B) (133)
5.4. Tabu Search (TS) (135)
5.5. Summary (135)
6. Unsupervised Learning Techniques (137)
6.1. Principal Component Analysis (PCA) (139)
6.2. Clustering Techniques (142)
6.3. Summary (155)
7. Model Complexity Optimization (157)
7.1. Introduction (157)
7.2. Bias/Variance Tradeoff (158)
7.3. Evaluating the Error and Alternatives (167)
7.4. Explicit Structure Optimization (176)
7.5. Regularzation: Implicit Structure Optimization (179)
7.6. Structured Models for Complexity Reduction (189)
7.7. Summary (200)
8. Summary of Part I (203)
Part II. Static Models
9. Introduction to Static Models (209)
9.1. Multivariable Systems (209)
9.2. Basis Function Formulation (210)
9.3. Extended Basis Function Formulation (215)
9.4. Static Test Process (216)
9.5. Evaluation Criteria (216)
10. Linear, Polynomial, and Look-Up Table Models (219)
10.1. Linear Models (219)
10.2. Polynomial Models (221)
10.3. Look-Up Table Models (224)
10.4. Summary (237)
11. Neural Networks (239)
11.1. Construction Mechanisms (242)
11.2. Multilayer Perceptron (MLP) Network (246)
11.3. Radial Function (RBF) Networks (264)
11.4. Other Neural Networks (286)
11.5. Summary (296)
12. Fuzzy and Neuro-Fuzzy Models (299)
12.1. Fuzzy Logic (299)
12.2. Types of Fuzzy Systems (304)
12.3. Neuro-Fuzzy (NF) Networks (310)
12.4. Neuro-Fuzzy Learning Schemes (323)
12.5. Summary (339)
13. Local Linear Neuro-Fuzzy Models: Fundamentals (341)
13.1. Basic Ideas (342)
13.2. Parameter Optimization of the Rule Consequents (351)
 13.3. Structure Optimization of the Rule Premises (362)
13.4. Summary (389)
14. Local Linear Neuro-Fuzzy Models: Advanced Aspects (391)
14.1. Different Input Spaces (391)
14.2. More Complex Local Models (397)
14.3. Structure Optimization of the Rule Consequents (404)
14.4. Interpolation and Extrapolation Behavior (408)
14.5. Global and Local Linearization (416)
14.6. Online Learning (420)
14.7. Errorbars and Design of Excitation Signals (430)
14.8. Hinging Hyperplanes (437)
14.9. Summary and Conclusions (444)
15. Summary of Part II (451)
Part III. Dynamic Models
16. Linear Dynamic System Identification (457)
16.1. Overview of Linear System Identification (458)
16.2. Excitation Signals (459)
16.3. General Model Structure (462)
16.4. Time Series Models (478)
16.5. Models with Output Feedback (482)
16.6. Models without Output Feedback (509)
16.7. Some Advanced Aspects (524)
16.8. Recursive Algorithms (531)
16.9. Determination of Dynamic Orders (536)
16.10. Multivariable Systems (537)
16.11. Closed-Loop Identification (541)
16.12. Summary (546)
17. Nonlinear Dynamic System Identification (547)
17.1. From Linear to Nonlinear System Identification (547)
 17.2. External Dynamics (549)
17.3. Internal Dynamics (563)
17.4. Parameter Scheduling Approach (564)
17.5. Training Recurrent Structures (564)
17.6. Multivariable Systems (568)
17.7. Excitation Signals (569)
17.8. Determination of Dynamic Orders (574)
17.9. Summary (576)
18. Classical Polynomial Approaches (579)
18.1. Properties of Dynamic Polynomial Models (580)
18.2. Kolmogorov-Gabor Polynomial Models (581)
18.3. Volterra-Series Models (582)
18.4. Parametric Volterra-Series Models (538)
18.5. NDE Models (583)
18.6. Hammerstein Models (584)
18.7. Wiener Models (585)
19. Dynamic Neural and Fuzzy Models (587)
19.1. Curse of Dimensionality (587)
19.2. Interpolation and Extrapolation Behavior (589)
19.3. Training (591)
19.4. Integration of a Linear Model (593)
19.5. Simulation Examples (594)
19.6. Summary (600)
20. Dynamic Local Linear Neuro-Fuzzy Models (601)
20.1. One-Step Prediction Error Versus Simulation Error (604)
20.2. Determination of the Premises (606)
20.3. Linerization (608)
20.4. Model Stability (613)
20.5. Dynamic LOLIMOT Simulation Studies (618)
20.6. Advanced Local Linear Methods and Models (626)
20.7. Local Linear Orthonormal Basis Functions Models (631)
20.8. Structure Optimization of the Rule Consequents (636)
20.9. Summary and Conclusions (640)
21. Neural Networks with Internal Dynamics (645)
21.1. Fully Recurrent Networks (645)
21.2. Partially Recurrent Networks (646)
21.3. State Recurrent Networks (647)
21.4. Locally Recurrent Globally Feedforward Networks (648)
21.5. Internal Versus External Dynamics (650)
Part IV. Applications
22. Applications of Static Models (655)
22.1. Driving Cycle (655)
22.2. Modeling and Optimization of Combustion Engine Exhaust (659)
22.3. Summary (674)
23. Applications of Dynamic Models (677)
23.1. Cooling Blast (677)
23.2. Diesel Engine Turbocharger (683)
23.3. Thermal Plant (691)
23.4. Summary (707)
24. Applications of Advanced Methods (709)
24.1. Nonlinear Model Predictive Control (709)
24.2. Online Adaptation (713)
24.3. Fault Detection (723)
24.4. Fault Diagnosis (729)
24.5. Reconfiguration (732)
A. Vectors and Matrices (735)
A.1. Vectors and Matrices Derivatives (735)
A.2. Gradient, Hessian, and Jacobian (737)
B. Statistics (739)
B.1. Deterministic and Random Variables (739)
B.2. Probability Density Function (pdf) (741)
B.3. Stochastic Processes and Ergodicity (743)
B.4. Expectation (745)
B.5. Variance (748)
B.6. Correlation and Covariance (749)
B.7. Properties of Estimators (753)
References (757)
Index (779)

 

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