Applied and computational control, signals, and circuits

摘要: 1 Discrete Event Systems: The State of the Art and New Directions.- 1.1 Introduction.- 1.2 DES Modeling Framework.- 1.3 Review in Theory.- 1.3.1 Supervisory Control.- 1.3.2 Max-Plus Algebra.- 1.3.3 Sample Path Analysis Performance Optimization.- 1.4 Directions 1.5 Decentralized Control 1.5.1 Some Key Issues.- 1.5.2 Optimization Problem Formulation.- 1.5.3 Distributed Estimation.- 1.5.4 Weak Convergence Analysis.- 1.6 Failure Diagnosis.- 1.6.1 Statement Problem.- 1.6.2 Survey Recent Literature.- 1.6.3 Presentation One Approach to 1.6.4 Issues for Future Research.- 1.7 Nondeterministic 1.7.1 Nondeterminism Semantics Untimed Models.- 1.7.2 Semantics.- 1.7.3 Trajectory 1.7.4 Bisimulation 1.7.5 Isomorphism 1.7.6 Discussion.- 1.8 Hybrid Systems Optimal 1.8.1 1.8.2 Using with Event-Driven Dynamics.- References.- 2 Array Algorithms forH2andH?Estimation.- 2.1 2.2H2Square Root Algorithms.- 2.2.1 Kalman Filtering.- 2.2.2 Square Arrays.- 2.3HO?Square 2.3.1H?Filtering.- 2.3.2 A Krein Space 2.3.3 J-Unitary Transformations.- 2.3.4 2.3.5 Central Filters.- 2.4H2Fast 2.5HO?Fast 2.5.1 General Case.- 2.5.2 2.6 Conclusion.- 2.A Unitary Hyperbolic Rotations.- 2.A.1 Elementary Householder 2.A.2 Circular or Givens 2.A.3 Fast 2.A.4 2.B Spaces.- 2.B.1 Geometric Interpretation.- 3 Nonuniqueness, Uncertainty, Complexity Modeling.- 3.1 3.2 Models 3.3 Nonuniqueness.- 3.4 Uncertainty.- 3.5 Complexity.- 3.6 Formulation Model Set Identification.- 3.7 Learning Optimization?.- 3.8 4 Iterative Control: An Expository Overview.- 4.1 4.2 Generic Description ILC.- 4.3 Two Illustrative Examples ILC 4.3.1 Linear Example.- 4.3.2 Adaptive Algorithm a Robotic Manipulator.- 4.4 Literature, Context, Terminology 4.4.1 Classifications 4.4.2 Connections Other Paradigms.- 4.5 Results.- 4.5.1 Basic Ideas.- 4.5.2 Nonlinear Systems.- 4.5.3 Robotics Applications.- 4.5.4 Approaches 4.6 Example: Combining Approaches.- 4.6.1 GMAW Model.- 4.6.2 ILC-Based Strategy.- 4.7 Conclusion: Past, Present, 5 FIR Filter Design via Spectral Factorization Convex 5.1 5.2 Factorization.- 5.3 Semi-infinite 5.4 Lowpass Design.- 5.5 Log-Chebychev Approximation.- 5.6 Magnitude Equalizer 5.7 Antenna Weight 5.8 Conclusions.- 5.A Appendix: 6 Subspace State-Space System Identification: 6.1 To Measure Is Know'.- 6.2 6.2.1 Rediscovering State.- 6.2.2 Structure 6.2.3 Steps 6.3 Comparing PEM Methods.- 6.4 Statistical Consistency 6.5 Extensions.- 6.5.1 Deterministic 6.5.2 Closed-loop 6.5.3 Frequency Domain 6.5.4 Identification Bilinear 6.6 Software DAISY.- 6.7 Conclusions Open Research Problems.- 7 Solution Methods Large Ill-Posed 7.1 7.2 Krylov 7.2.1 Standard Conjugate Gradient Algorithm.- 7.2.2 Inconsistent 7.3 Tikhonov Regularization.- 7.3.1 7.3.2 Based on Method.- 7.3.3 Explicit Approximation Function.- 7.3.4 Comparison Expansion 7.3.5 Total Variation Norm.- 7.4 Exponential 7.5 Implicitly Defined Functions.- 7.5.1 Landweber Iteration.- 7.5.2 Truncated 7.5.3 Regularizing Preconditioned 7.6 Toward Black Box.- 7.6.1 Computation Regularization Parameter.- 7.6.2 7.7 Computed Examples.- 8 Wavelet-Based Image Coding: 8.1 8.1.1 Compression.- 8.2 Quantization.- 8.2.1 Vector 8.2.2 Quantizers.- 8.2.3 Sphere Covering Density Shaping.- 8.2.4 Cross-Variable Dependencies.- 8.2.5 Fractional Bitrates.- 8.3 Transform Coding.- 8.3.1 Karhunen-Loeve Transform.- 8.3.2 Bit Allocation.- 8.3.3 Optimality 8.3.4 Cosine 8.3.5 Subband Transforms.- 8.4 Wavelets: Different Perspective.- 8.4.1 Multiresolution Analyses.- 8.4.2 Wavelets.- 8.4.3 Recurrence Relations.- 8.4.4 Wavelet Transforms vs. Decompositions.- 8.4.5 Properties.- 8.5 Coder.- 8.5.1 Choice Basis.- 8.5.2 Boundaries.- 8.5.3 8.5.4 Entropy 8.5.5 8.5.6 Perceptually Weighted Error Measures.- 8.6 Extending Coder Paradigm.- 8.7 Zerotree 8.7.1 Shapiro Said-Pearlman Coders.- 8.7.2 Zerotrees Rate-Distortion 8.8 Space-Frequency 8.8.1 Packets.- 8.8.2 8.8.3 8.9 Utilizing Intra-band 8.9.1 Trellis Coded 8.9.2 TCQ 8.9.3 Mixture 8.10 Trends.- 8.11 Summary 9 Reduced-Order Techniques Subspaces Their Use Circuit Simulation.- 9.1 9.2 Dynamical 9.2.1 9.2.2 9.2.3 Reduction Matrix.- 9.3 9.3.1 Equations.- 9.3.2 Subcircuits Linearized Circuits.- 9.3.3 RLC 9.4 Moment Matching.- 9.4.1 Assumptions Convention.- 9.4.2 Single Starting Vectors.- 9.4.3 Connection 9.4.4 Multiple 9.5 Lanczos Process.- 9.5.1 Classical 9.5.2 Lanczos-Type 9.5.3 Exploiting Symmetry.- 9.6 Lanczos-Based 9.6.1 Lanczos-Pade Connection.- 9.6.2 Multi-Input Multi-Output 9.6.3 Stability Passivity.- 9.6.4 PVL7r: Post-Processing PVL.- 9.6.5 Passive from SyMPVL.- 9.6.6 How Achieve Passivity Practice.- 9.6.7 9.7 Arnoldi 9.8 Arnoldi-Based 9.9 Circuit-Noise Computations.- 9.9.1 9.9.2 Reformulation as Transfer 9.9.3 PVL 9.10 Concluding Remarks.- 10 SLICOT-A Subroutine Library 10.1 10.2 Why Do We Need More Than MATLAB Numerics?.- 10.2.1 Limitations MATLAB.- 10.2.2 Production Quality Numerical Software.- 10.2.3 Low-Level Reusability Fortran Libraries.- 10.2.4 Preserving 10.3 Retrospect.- 10.3.1 Short History 10.3.2 Libraries RASP SLICOT: Present Status.- 10.3.3 RASP/SLICOT Mutual Compatibility Concept.- 10.4 SLICOT.- 10.4.1 Library.- 10.4.2 10.4.3 User Manual.- 10.4.4 Implementation Documentation Standards.- 10.4.5 Benchmarks.- 10.5 Contents 10.5.1 Current 10.5.2 Development Public Release 10.5.3 In Queue.- 10.6 10.7 - NICONET.- 10.7.1 Objectives Exploratory Phase 10.7.2 Performant CACSD.- 10.7.3 Integration User-Friendly Environment.- 10.7.4 Benchmarking Testing an Industrial 10.7.5 Information Dissemination Access 10.7.6 Phase.- 10.8 10.A SLICOT 3.0.- 10.B Electronic Related Literature.