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 Simon, Dan, 1960
 Hoboken, N.J. : WileyInterscience, c2006.
 Description
 Book โ xxvi, 526 p. : ill. ; 26 cm.
 Summary

 Acknowledgments.Acronyms.List of algorithms.Introduction.PART I INTRODUCTORY MATERIAL.1 Linear systems theory.1.1 Matrix algebra and matrix calculus.1.1.1 Matrix algebra.1.1.2 The matrix inversion lemma.1.1.3 Matrix calculus.1.1.4 The history of matrices.1.2 Linear systems.1.3 Nonlinear systems.1.4 Discretization.1.5 Simulation.1.5.1 Rectangular integration.1.5.2 Trapezoidal integration.1.5.3 RungeKutta integration.1.6 Stability.1.6.1 Continuoustime systems.1.6.2 Discretetime systems.1.7 Controllability and observability.1.7.1 Controllability.1.7.2 Observability.1.7.3 Stabilizability and detectability.1.8 Summary.Problems.Probability theory.2.1 Probability.2.2 Random variables.2.3 Transformations of random variables.2.4 Multiple random variables.2.4.1 Statistical independence.2.4.2 Multivariate statistics.2.5 Stochastic Processes.2.6 White noise and colored noise.2.7 Simulating correlated noise.2.8 Summary.Problems.3 Least squares estimation.3.1 Estimation of a constant.3.2 Weighted least squares estimation.3.3 Recursive least squares estimation.3.3.1 Alternate estimator forms.3.3.2 Curve fitting.3.4 Wiener filtering.3.4.1 Parametric filter optimization.3.4.2 General filter optimization.3.4.3 Noncausal filter optimization.3.4.4 Causal filter optimization.3.4.5 Comparison.3.5 Summary.Problems.4 Propagation of states and covariances.4.1 Discretetime systems.4.2 Sampleddata systems.4.3 Continuoustime systems.4.4 Summary.Problems.PART II THE KALMAN FILTER.5 The discretetime Kalman filter.5.1 Derivation of the discretetime Kalman filter.5.2 Kalman filter properties.5.3 Onestep Kalman filter equations.5.4 Alternate propagation of covariance.5.4.1 Multiple state systems.5.4.2 Scalar systems.5.5 Divergence issues.5.6 Summary.Problems.6 Alternate Kalman filter formulations.6.1 Sequential Kalman filtering.6.2 Information filtering.6.3 Square root filtering.6.3.1 Condition number.6.3.2 The square root timeupdate equation.6.3.3 Potter's square root measurementupdate equation.6.3.4 Square root measurement update via triangularization.6.3.5 Algorithms for orthogonal transformations.6.4 UD filtering.6.4.1 UD filtering: The measurementupdate equation.6.4.2 UD filtering: The timeupdate equation.6.5 Summary.Problems.7 Kalman filter generalizations.7.1 Correlated process and measurement noise.7.2 Colored process and measurement noise.7.2.1 Colored process noise.7.2.2 Colored measurement noise: State augmentation.7.2.3 Colored measurement noise: Measurement differencing.7.3 Steadystate filtering.7.3.1 aP filtering.7.3.2 aPy filtering.7.3.3 A Hamiltonian approach to steadystate filtering.7.4 Kalman filtering with fading memory.7.5 Constrained Kalman filtering.7.5.1 Model reduction.7.5.2 Perfect measurements.7.5.3 Projection approaches.7.5.4 A pdf truncation approach.7.6 Summary.Problems.8 The continuoustime Kalman filter.8.1 Discretetime and continuoustime white noise.8.1.1 Process noise.8.1.2 Measurement noise.8.1.3 Discretized simulation of noisy continuoustime systems.8.2 Derivation of the continuoustime Kalman filter.8.3 Alternate solutions to the Riccati equation.8.3.1 The transition matrix approach.8.3.2 The Chandrasekhar algorithm.8.3.3 The square root filter.8.4 Generalizations of the continuoustime filter.8.4.1 Correlated process and measurement noise.8.4.2 Colored measurement noise8.5 The steadystate continuoustime Kalman filter8.5.1 The algebraic Riccati equation.8.5.2 The Wiener filter is a Kalman filter.8.5.3 Duality.8.6 Summary.Problems.9 Optimal smoothing.9.1 An alternate form for the Kalman filter.9.2 Fixedpoint smoothing.9.2.1 Estimation improvement due to smoothing.9.2.2 Smoothing constant states.9.3 Fixedlag smoothing.9.4 Fixedinterval smoothing.9.4.1 Forwardbackward smoothing.9.4.2 RTS smoothing.9.5 Summary.Problems.10 Additional topics in Kalman filtering.10.1 Verifying Kalman filter performance.10.2 Multiplemodel estimation.10.3 Reducedorder Kalman filtering.10.3.1 Anderson's approach to reducedorder filtering.10.3.2 The reducedorder SchmidtKalman filter.10.4 Robust Kalman filtering.10.5 Delayed measurements and synchronization errors.10.5.1 A statistical derivation of the Kalman filter.10.5.2 Kalman filtering with delayed measurements.10.6 Summary.Problems.PART III THE H, FILTER.11 The H, filter.11.1 Introduction.11.1.1 An alternate form for the Kalman filter.11.1.2 Kalman filter limitations.11.2 Constrained optimization.11.2.1 Static constrained optimization.11.2.2 Inequality constraints.11.2.3 Dynamic constrained optimization.11.3 A game theory approach to H, filtering.11.3.1 Stationarity with respect to xo and wk.11.3.2 Stationarity with respect to 2 and y.11.3.3 A comparison of the Kalman and H, filters.11.3.4 Steadystate H, filtering.11.3.5 The transfer function bound of the H, filter.11.4 The continuoustime H, filter.11.5 Transfer function approaches.11.6 Summary.Problems.12 Additional topics in H, filtering.12.1 Mixed KalmanIH, filtering.12.2 Robust Kalman/H, filtering.12.3 Constrained H, filtering.12.4 Summary.Problems.PART IV NONLINEAR FILTERS.13 Nonlinear Kalman filtering.13.1 The linearized Kalman filter.13.2 The extended Kalman filter.13.2.1 The continuoustime extended Kalman filter.13.2.2 The hybrid extended Kalman filter.13.2.3 The discretetime extended Kalman filter.13.3 Higherorder approaches.13.3.1 The iterated extended Kalman filter.13.3.2 The secondorder extended Kalman filter.13.3.3 Other approaches.13.4 Parameter estimation.13.5 Summary.Problems.14 The unscented Kalman filter.14.1 Means and covariances of nonlinear transformations.14.1.1 The mean of a nonlinear transformation.14.1.2 The covariance of a nonlinear transformation.14.2 Unscented transformations.14.2.1 Mean approximation.14.2.2 Covariance approximation.14.3 Unscented Kalman filtering.14.4 Other unscented transformations.14.4.1 General unscented transformations.14.4.2 The simplex unscented transformation.14.4.3 The spherical unscented transformation.14.5 Summary.Problems.15 The particle filter.15.1 Bayesian state estimation.15.2 Particle filtering.15.3 Implementation issues.15.3.1 Sample impoverishment.15.3.2 Particle filtering combined with other filters.15.4 Summary.Problems.Appendix A: Historical perspectives.Appendix B: Other books on Kalman filtering.Appendix C: State estimation and the meaning of life.References.Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA402.3 .S5446 2006  Unknown 
 Ma, Haiping author.
 London, UK : ISTE, Ltd. ; Hoboken, NJ : John Wiley & Sons, Inc. 2017.
 Description
 Book โ 1 online resource.
 Summary

 Chapter 1. The Science of Biogeography 1 1.1. Introduction 1 1.2. Island biogeography 3 1.3. Influence factors for biogeography 6
 Chapter 2. Biogeography and Biological Optimization 11 2.1. A mathematical model of biogeography 11 2.2. Biogeography as an optimization process 16 2.3. Biological optimization 19 2.3.1. Genetic algorithms 19 2.3.2. Evolution strategies 20 2.3.3. Particle swarm optimization 21 2.3.4. Artificial bee colony algorithm 22 2.4. Conclusion 23
 Chapter 3. A Basic BBO Algorithm 25 3.1. BBO definitions and algorithm 25 3.1.1. Migration 26 3.1.2. Mutation 27 3.1.3. BBO implementation 27 3.2. Differences between BBO and other optimization algorithms 35 3.2.1. BBO and genetic algorithms 35 3.2.2. BBO and other algorithms 36 3.3. Simulations 37 3.4. Conclusion 44
 Chapter 4. BBO Extensions 45 4.1. Migration curves 45 4.2. Blended migration 49 4.3. Other approaches to BBO 51 4.4. Applications 56 4.5. Conclusion 59
 Chapter 5. BBO as a Markov Process 61 5.1. Markov definitions and notations 61 5.2. Markov model of BBO 72 5.3. BBO convergence 79 5.4. Markov models of BBO extensions 90 5.5. Conclusions 99
 Chapter 6. Dynamic System Models of BBO 103 6.1. Basic notation 103 6.2. Dynamic system models of BBO 105 6.3. Applications to benchmark problems 119 6.4. Conclusions 122
 Chapter 7. Statistical Mechanics Approximations of BBO 123 7.1. Preliminary foundation 123 7.2. Statistical mechanics model of BBO 128 7.2.1. Migration 128 7.2.2. Mutation 134 7.3. Further discussion 141 7.3.1. Finite population effects 141 7.3.2. Separable fitness functions 142 7.4. Conclusions 143
 Chapter 8. BBO for Combinatorial Optimization 145 8.1. Traveling salesman problem 147 8.2. BBO for the TSP 148 8.2.1. Population initialization 148 8.2.2. Migration in the TSP 150 8.2.3. Mutation in the TSP 157 8.2.4. Implementation framework 159 8.3. Graph coloring 163 8.4. Knapsack problem 165 8.5. Conclusion 167
 Chapter 9. Constrained BBO 169 9.1. Constrained optimization 170 9.2. Constrainthandling methods 172 9.2.1. Static penalty methods 172 9.2.2. Superiority of feasible points 173 9.2.3. The eclectic evolutionary algorithm 174 9.2.4. Dynamic penalty methods 174 9.2.5. Adaptive penalty methods 176 9.2.6. The nichedpenalty approach 177 9.2.7. Stochastic ranking 178 9.2.8. level comparisons 178 9.3. BBO for constrained optimization 179 9.4. Conclusion 185
 Chapter 10. BBO in Noisy Environments 187 10.1. Noisy fitness functions 188 10.2. Influence of noise on BBO 190 10.3. BBO with resampling 193 10.4. The Kalman BBO 196 10.5. Experimental results 199 10.6. Conclusion 201
 Chapter 11. Multiobjective BBO 203 11.1. Multiobjective optimization problems 204 11.2. Multiobjective BBO 211 11.2.1. Vector evaluated BBO 211 11.2.2. Nondominated sorting BBO 213 11.2.3. Niched Pareto BBO 216 11.2.4. Strength Pareto BBO 218 11.3. Realworld applications 223 11.3.1. Warehouse scheduling model 223 11.3.2. Optimization of warehouse scheduling 229 11.4. Conclusion 231
 Chapter 12. Hybrid BBO Algorithms 233 12.1. Oppositionbased BBO 234 12.1.1. Opposition definitions and concepts 234 12.1.2. Oppositional BBO 236 12.1.3. Experimental results 238 12.2. BBO with local search 240 12.2.1. Local search methods 240 12.2.2. Simulation results 245 12.3. BBO with other EAs 247 12.3.1. Iterationlevel hybridization 247 12.3.2. Algorithmlevel hybridization 250 12.3.3. Experimental results 254 12.4. Conclusion 256 Appendices 259 Appendix A. Unconstrained Benchmark Functions 261 Appendix B. Constrained Benchmark Functions 265 Appendix C. Multiobjective Benchmark Functions 289 Bibliography 309 Index 325.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
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