Amazon cover image
Image from Amazon.com

Cognitive Dynamic Systems : Perception-action Cycle, Radar and Radio.

By: Publisher: Cambridge : Cambridge University Press, 2012Copyright date: ©2012Description: 1 online resource (324 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781139114684
Subject(s): Genre/Form: Additional physical formats: Print version:: Cognitive Dynamic Systems : Perception-action Cycle, Radar and RadioDDC classification:
  • 003.7
LOC classification:
  • Q325 .H39 2012
Online resources:
Contents:
Cover -- Cognitive Dynamic Systems -- Title -- Copyright -- Contents -- Preface -- Acknowledgments -- 1: Introduction -- 1.1 Cognitive dynamic systems -- 1.2 The perception-action cycle -- 1.3 Cognitive dynamic wireless systems: radar and radio -- 1.3.1 Cognitive radar -- 1.3.2 Cognitive radio -- 1.4 Illustrative cognitive radar experiment -- 1.4.1 The experiment -- 1.4.2 The environment -- 1.4.3 The radar -- 1.4.4 State-space model -- 1.4.5 Simulation results -- 1.5 Principle of information preservation -- 1.5.1 Feedback information -- 1.5.2 Bayesian filtering of the measurements -- 1.5.3 Information preservation through cognition -- 1.5.4 Concluding remarks -- 1.6 Organization of the book -- Notes and practical references -- 2: The perception-action cycle -- 2.1 Perception -- 2.1.1 Functional integration-across-time property of cognition -- 2.2 Memory -- 2.2.1 Perceptual memory -- 2.2.2 Executive memory -- 2.2.3 Final reciprocal coupling to complete the cognitive information-processing cycle -- 2.2.4 Roles of memory in cognition -- 2.3 Working memory -- 2.4 Attention -- 2.4.1 Roles of attention in cognition -- 2.5 Intelligence -- 2.5.1 Efficiency of processing information -- 2.5.2 Synchronized cognitive information processing -- 2.5.3 The role of intelligence in cognition -- 2.6 Practical benefits of hierarchy in the perception-action cycle -- 2.7 Neural networks for parallel distributed cognitive information processing -- 2.7.1 Benefits of neural networks -- 2.7.2 Models of a neuron -- 2.7.3 Multilayer feedforward networks -- 2.8 Associative learning process for memory construction -- 2.8.1 Pattern association -- 2.8.2 Replicator (identity) mapping -- 2.9 Back-propagation algorithm -- 2.9.1 Summary of the back-propagation algorithm -- 2.10 Recurrent multilayer perceptrons -- 2.11 Self-organized learning -- 2.11.1 Hebb's postulate of learning.
2.11.2 Generalized Hebbian algorithm -- 2.11.3 Signal-flow graph of the GHA -- 2.12 Summary and discussion -- 2.12.1 Cognition -- 2.12.2 Two different views of perception -- Notes and practical references -- 3: Power-spectrum estimation for sensing the environment -- 3.1 The power spectrum -- 3.2 Power spectrum estimation -- 3.2.1 Parametric methods -- 3.2.2 Nonparametric methods -- 3.3 Multitaper method -- 3.3.1 Attributes of multitaper spectral estimation -- 3.3.2 Multitaper spectral estimation theory -- 3.3.3 Adaptive modification of multitaper spectral estimation -- 3.3.4 Summarizing remarks on the MTM -- 3.3.5 Comparison of the MTM with other spectral estimators -- 3.4 Space-time processing -- 3.4.1 Physical interpretation of the action performed by the MTM-SVD processor -- 3.5 Time-frequency analysis -- 3.5.1 Theoretical background of nonstationarity -- 3.5.2 Spectral coherences of nonstationary processes based on the Loève transform -- 3.5.3 Two special cases of the dynamic spectrum D (t0, f ) -- 3.5.3.1 Wigner-Ville distribution -- 3.5.3.2 Cyclic power spectrum -- 3.5.4 Instrumentation for computing Loève spectral correlations -- 3.6 Cyclostationarity -- 3.6.1 Fourier framework of cyclic statistics -- 3.6.2 Instrumentation for computing the Fourier spectral correlations -- 3.6.3 Relationship between the Fourier and Loève spectral coherences -- 3.6.4 Contrasting the two theories on cyclostationarity -- 3.7 Harmonic F-test for spectral line components -- 3.7.1 Brief outline of the F-test -- 3.7.2 Point regression single-line F -test -- 3.8 Summary and discussion -- 3.8.1 The MTM for power spectrum estimation -- 3.8.2 Extensions of the MTM -- 3.8.3 Concluding remarks -- 3.8.3.1 Mathematical framework -- 3.8.3.2 Practical requirement -- Notes and practical references -- 4: Bayesian filtering for state estimation of the environment.
4.1 Probability, conditional probability, and Bayes' rule -- 4.1.1 Conditional probability -- 4.1.2 Bayes' rule -- 4.2 Bayesian inference and importance of the posterior -- 4.2.1 Likelihood -- 4.2.2 The likelihood principle -- 4.2.3 Sufficient statistic -- 4.3 Parameter estimation and hypothesis testing: the MAP rule -- 4.3.1 Parameter estimation -- 4.3.2 Hypothesis testing -- 4.3.3 Summarizing remarks on Bayesian inference -- 4.4 State-space models -- 4.4.1 Sequential state-estimation problem -- 4.4.2 Hierarchy of state-space models -- 4.5 The Bayesian filter -- 4.5.1 Optimality of the Bayesian filter -- 4.5.2 Approximation of the Bayesian filter -- 4.6 Extended Kalman filter -- 4.6.1 Summarizing remarks on the extended Kalman filter -- 4.7 Cubature Kalman filters -- 4.7.1 Converting to spherical-radial integration -- 4.7.2 Spherical rule -- 4.7.3 Radial rule -- 4.7.4 Spherical-radial rule -- 4.7.5 Derivation of the CKF -- 4.7.6 Properties of the CKF -- 4.7.7 Summarizing remarks on the CKF -- 4.8 On the relationship between the cubature and unscented Kalman filters -- 4.8.1 Unscented Kalman filter -- 4.8.2 On the relationship between UKF and CKF -- 4.8.2.1 Theoretical considerations -- 4.8.2.2 Geometric considerations -- 4.8.2.3 Curse-of-dimensionality problem -- 4.8.3 Summarizing remarks -- 4.9 The curse of dimensionality -- 4.9.1 Case study on the curse-of-dimensionality problem -- 4.10 Recurrent multilayer perceptrons: an application for state estimation -- 4.10.1 Description of the supervised training framework using the EKF -- 4.10.2 The EKF algorithm -- 4.10.3 Decoupled EKF -- 4.10.4 Summarizing remarks on the EKF -- 4.10.5 Supervised training of neural networks using the CKF -- 4.10.6 Adaptivity considerations -- 4.11 Summary and discussion -- 4.11.1 Optimal Bayesian filter -- 4.11.2 Extended Kalman filter -- 4.11.3 Cubature Kalman filters.
Notes and practical references -- 5: Dynamic programming for action in the environment -- 5.1 Markov decision processes -- 5.1.1 The basic problem -- 5.2 Bellman's optimality criterion -- 5.2.1 Dynamic-programming algorithm -- 5.2.2 Bellman's optimality equation -- 5.3 Policy iteration -- 5.3.1 Formulation of the policy iteration algorithm -- 5.4 Value iteration -- 5.4.1 Formulation of the value iteration algorithm -- 5.5 Approximate dynamic programming for problems with imperfect state information -- 5.5.1 Basics of problems with imperfect state information -- 5.5.2 Reformulation of the imperfect state-information problem as a perfect state-information problem -- 5.6 Reinforcement learning viewed as approximate dynamic programming -- 5.7 Q-learning -- 5.7.1 Summarizing remarks -- 5.8 Temporal-difference learning -- 5.8.1 Multistep TD learning -- 5.8.2 Eligible traces -- 5.8.3 Two limiting cases of TD learning -- 5.8.4 Summarizing remarks -- 5.9 On the relationships between temporal-difference learning and dynamic programming -- 5.9.1 λ-return -- 5.10 Linear function approximations of dynamic programming -- 5.11 Linear GQ(λ) for predictive learning -- 5.11.1 Objective function setting the stage for approximation -- 5.11.2 The GQ(λ) algorithm -- 5.11.3 Weight-doubling trick -- 5.11.4 Eligibility traces vector -- 5.11.5 New action-state feature vector -- 5.11.6 Summarizing remarks -- 5.11.7 Practical considerations -- 5.12 Summary and discussion -- 5.12.1 Bellman's dynamic programming -- 5.12.2 Imperfect state information -- 5.12.3 Reinforcement learning -- 5.12.4 Linear GQ(k) algorithm -- 5.12.5 Greedy-GQ -- 5.12.6 New generation of approximate dynamic programming algorithms: linear GQ methods -- Notes and practical references -- 6: Cognitive radar -- 6.1 Three classes of radars defined -- 6.2 The perception-action cycle.
6.3 Baseband model of radar signal transmission -- 6.3.1 Baseband models of the transmitted and received signals -- 6.3.2 Bank of matched fi lters and envelope detectors -- 6.3.3 State-space model of the target -- 6.3.4 Dependence of measurement noise on the transmitted signal -- 6.3.5 Closing remarks -- 6.4 System design considerations -- 6.5 Cubature Kalman filter for target-state estimation -- 6.5.1 Cubature rule of third degree -- 6.5.2 Probability-distribution flow-graph of the Bayesian filter -- 6.5.3 Time update -- 6.5.4 Measurement update -- 6.5.5 Summarizing remarks -- 6.6 Transition from perception to action -- 6.6.1 Feedback information about the target -- 6.6.2 Posterior expected error covariance matrix -- 6.7 Cost-to-go function -- 6.7.1 Cost-to-go function using mean-square error -- 6.7.2 Cost-to-go function using Shannon's entropy -- 6.7.3 Another information-theoretic viewpoint of the entropy-based cost-to-go function -- 6.8 Cyclic directed information-flow -- 6.8.1 Bottom-up transmission path -- 6.8.2 Top-down transmission path -- 6.9 Approximate dynamic programming for optimal control -- 6.9.1 Step 1: cost-to-go function for compressing information about the radar environment -- 6.9.2 Step 2: approximation in the measurement space -- 6.9.3 Special case: dynamic optimization -- 6.10 The curse-of-dimensionality problem -- 6.11 Two-dimensional grid for waveform library -- 6.12 Case study: tracking a falling object in space -- 6.12.1 Modeling the reentry problem -- 6.12.2 Radar configurations -- 6.12.3 Performance metric -- 6.12.4 Simulation results -- 6.12.5 Comments on the simulation results -- 6.13 Cognitive radar with single layer of memory -- 6.13.1 Cyclic directed information flow in cognitive radar with single layer of memory -- 6.13.2 Communication among subsystems in cognitive radar.
6.13.3 Communications between scene analyzer and perceptual memory.
Summary: A groundbreaking book from Simon Haykin, setting out the fundamental ideas and highlighting a range of future research directions.
Holdings
Item type Current library Call number Status Date due Barcode Item holds
Ebrary Ebrary Afghanistan Available EBKAF00060535
Ebrary Ebrary Algeria Available
Ebrary Ebrary Cyprus Available
Ebrary Ebrary Egypt Available
Ebrary Ebrary Libya Available
Ebrary Ebrary Morocco Available
Ebrary Ebrary Nepal Available EBKNP00060535
Ebrary Ebrary Sudan Available
Ebrary Ebrary Tunisia Available
Total holds: 0

Cover -- Cognitive Dynamic Systems -- Title -- Copyright -- Contents -- Preface -- Acknowledgments -- 1: Introduction -- 1.1 Cognitive dynamic systems -- 1.2 The perception-action cycle -- 1.3 Cognitive dynamic wireless systems: radar and radio -- 1.3.1 Cognitive radar -- 1.3.2 Cognitive radio -- 1.4 Illustrative cognitive radar experiment -- 1.4.1 The experiment -- 1.4.2 The environment -- 1.4.3 The radar -- 1.4.4 State-space model -- 1.4.5 Simulation results -- 1.5 Principle of information preservation -- 1.5.1 Feedback information -- 1.5.2 Bayesian filtering of the measurements -- 1.5.3 Information preservation through cognition -- 1.5.4 Concluding remarks -- 1.6 Organization of the book -- Notes and practical references -- 2: The perception-action cycle -- 2.1 Perception -- 2.1.1 Functional integration-across-time property of cognition -- 2.2 Memory -- 2.2.1 Perceptual memory -- 2.2.2 Executive memory -- 2.2.3 Final reciprocal coupling to complete the cognitive information-processing cycle -- 2.2.4 Roles of memory in cognition -- 2.3 Working memory -- 2.4 Attention -- 2.4.1 Roles of attention in cognition -- 2.5 Intelligence -- 2.5.1 Efficiency of processing information -- 2.5.2 Synchronized cognitive information processing -- 2.5.3 The role of intelligence in cognition -- 2.6 Practical benefits of hierarchy in the perception-action cycle -- 2.7 Neural networks for parallel distributed cognitive information processing -- 2.7.1 Benefits of neural networks -- 2.7.2 Models of a neuron -- 2.7.3 Multilayer feedforward networks -- 2.8 Associative learning process for memory construction -- 2.8.1 Pattern association -- 2.8.2 Replicator (identity) mapping -- 2.9 Back-propagation algorithm -- 2.9.1 Summary of the back-propagation algorithm -- 2.10 Recurrent multilayer perceptrons -- 2.11 Self-organized learning -- 2.11.1 Hebb's postulate of learning.

2.11.2 Generalized Hebbian algorithm -- 2.11.3 Signal-flow graph of the GHA -- 2.12 Summary and discussion -- 2.12.1 Cognition -- 2.12.2 Two different views of perception -- Notes and practical references -- 3: Power-spectrum estimation for sensing the environment -- 3.1 The power spectrum -- 3.2 Power spectrum estimation -- 3.2.1 Parametric methods -- 3.2.2 Nonparametric methods -- 3.3 Multitaper method -- 3.3.1 Attributes of multitaper spectral estimation -- 3.3.2 Multitaper spectral estimation theory -- 3.3.3 Adaptive modification of multitaper spectral estimation -- 3.3.4 Summarizing remarks on the MTM -- 3.3.5 Comparison of the MTM with other spectral estimators -- 3.4 Space-time processing -- 3.4.1 Physical interpretation of the action performed by the MTM-SVD processor -- 3.5 Time-frequency analysis -- 3.5.1 Theoretical background of nonstationarity -- 3.5.2 Spectral coherences of nonstationary processes based on the Loève transform -- 3.5.3 Two special cases of the dynamic spectrum D (t0, f ) -- 3.5.3.1 Wigner-Ville distribution -- 3.5.3.2 Cyclic power spectrum -- 3.5.4 Instrumentation for computing Loève spectral correlations -- 3.6 Cyclostationarity -- 3.6.1 Fourier framework of cyclic statistics -- 3.6.2 Instrumentation for computing the Fourier spectral correlations -- 3.6.3 Relationship between the Fourier and Loève spectral coherences -- 3.6.4 Contrasting the two theories on cyclostationarity -- 3.7 Harmonic F-test for spectral line components -- 3.7.1 Brief outline of the F-test -- 3.7.2 Point regression single-line F -test -- 3.8 Summary and discussion -- 3.8.1 The MTM for power spectrum estimation -- 3.8.2 Extensions of the MTM -- 3.8.3 Concluding remarks -- 3.8.3.1 Mathematical framework -- 3.8.3.2 Practical requirement -- Notes and practical references -- 4: Bayesian filtering for state estimation of the environment.

4.1 Probability, conditional probability, and Bayes' rule -- 4.1.1 Conditional probability -- 4.1.2 Bayes' rule -- 4.2 Bayesian inference and importance of the posterior -- 4.2.1 Likelihood -- 4.2.2 The likelihood principle -- 4.2.3 Sufficient statistic -- 4.3 Parameter estimation and hypothesis testing: the MAP rule -- 4.3.1 Parameter estimation -- 4.3.2 Hypothesis testing -- 4.3.3 Summarizing remarks on Bayesian inference -- 4.4 State-space models -- 4.4.1 Sequential state-estimation problem -- 4.4.2 Hierarchy of state-space models -- 4.5 The Bayesian filter -- 4.5.1 Optimality of the Bayesian filter -- 4.5.2 Approximation of the Bayesian filter -- 4.6 Extended Kalman filter -- 4.6.1 Summarizing remarks on the extended Kalman filter -- 4.7 Cubature Kalman filters -- 4.7.1 Converting to spherical-radial integration -- 4.7.2 Spherical rule -- 4.7.3 Radial rule -- 4.7.4 Spherical-radial rule -- 4.7.5 Derivation of the CKF -- 4.7.6 Properties of the CKF -- 4.7.7 Summarizing remarks on the CKF -- 4.8 On the relationship between the cubature and unscented Kalman filters -- 4.8.1 Unscented Kalman filter -- 4.8.2 On the relationship between UKF and CKF -- 4.8.2.1 Theoretical considerations -- 4.8.2.2 Geometric considerations -- 4.8.2.3 Curse-of-dimensionality problem -- 4.8.3 Summarizing remarks -- 4.9 The curse of dimensionality -- 4.9.1 Case study on the curse-of-dimensionality problem -- 4.10 Recurrent multilayer perceptrons: an application for state estimation -- 4.10.1 Description of the supervised training framework using the EKF -- 4.10.2 The EKF algorithm -- 4.10.3 Decoupled EKF -- 4.10.4 Summarizing remarks on the EKF -- 4.10.5 Supervised training of neural networks using the CKF -- 4.10.6 Adaptivity considerations -- 4.11 Summary and discussion -- 4.11.1 Optimal Bayesian filter -- 4.11.2 Extended Kalman filter -- 4.11.3 Cubature Kalman filters.

Notes and practical references -- 5: Dynamic programming for action in the environment -- 5.1 Markov decision processes -- 5.1.1 The basic problem -- 5.2 Bellman's optimality criterion -- 5.2.1 Dynamic-programming algorithm -- 5.2.2 Bellman's optimality equation -- 5.3 Policy iteration -- 5.3.1 Formulation of the policy iteration algorithm -- 5.4 Value iteration -- 5.4.1 Formulation of the value iteration algorithm -- 5.5 Approximate dynamic programming for problems with imperfect state information -- 5.5.1 Basics of problems with imperfect state information -- 5.5.2 Reformulation of the imperfect state-information problem as a perfect state-information problem -- 5.6 Reinforcement learning viewed as approximate dynamic programming -- 5.7 Q-learning -- 5.7.1 Summarizing remarks -- 5.8 Temporal-difference learning -- 5.8.1 Multistep TD learning -- 5.8.2 Eligible traces -- 5.8.3 Two limiting cases of TD learning -- 5.8.4 Summarizing remarks -- 5.9 On the relationships between temporal-difference learning and dynamic programming -- 5.9.1 λ-return -- 5.10 Linear function approximations of dynamic programming -- 5.11 Linear GQ(λ) for predictive learning -- 5.11.1 Objective function setting the stage for approximation -- 5.11.2 The GQ(λ) algorithm -- 5.11.3 Weight-doubling trick -- 5.11.4 Eligibility traces vector -- 5.11.5 New action-state feature vector -- 5.11.6 Summarizing remarks -- 5.11.7 Practical considerations -- 5.12 Summary and discussion -- 5.12.1 Bellman's dynamic programming -- 5.12.2 Imperfect state information -- 5.12.3 Reinforcement learning -- 5.12.4 Linear GQ(k) algorithm -- 5.12.5 Greedy-GQ -- 5.12.6 New generation of approximate dynamic programming algorithms: linear GQ methods -- Notes and practical references -- 6: Cognitive radar -- 6.1 Three classes of radars defined -- 6.2 The perception-action cycle.

6.3 Baseband model of radar signal transmission -- 6.3.1 Baseband models of the transmitted and received signals -- 6.3.2 Bank of matched fi lters and envelope detectors -- 6.3.3 State-space model of the target -- 6.3.4 Dependence of measurement noise on the transmitted signal -- 6.3.5 Closing remarks -- 6.4 System design considerations -- 6.5 Cubature Kalman filter for target-state estimation -- 6.5.1 Cubature rule of third degree -- 6.5.2 Probability-distribution flow-graph of the Bayesian filter -- 6.5.3 Time update -- 6.5.4 Measurement update -- 6.5.5 Summarizing remarks -- 6.6 Transition from perception to action -- 6.6.1 Feedback information about the target -- 6.6.2 Posterior expected error covariance matrix -- 6.7 Cost-to-go function -- 6.7.1 Cost-to-go function using mean-square error -- 6.7.2 Cost-to-go function using Shannon's entropy -- 6.7.3 Another information-theoretic viewpoint of the entropy-based cost-to-go function -- 6.8 Cyclic directed information-flow -- 6.8.1 Bottom-up transmission path -- 6.8.2 Top-down transmission path -- 6.9 Approximate dynamic programming for optimal control -- 6.9.1 Step 1: cost-to-go function for compressing information about the radar environment -- 6.9.2 Step 2: approximation in the measurement space -- 6.9.3 Special case: dynamic optimization -- 6.10 The curse-of-dimensionality problem -- 6.11 Two-dimensional grid for waveform library -- 6.12 Case study: tracking a falling object in space -- 6.12.1 Modeling the reentry problem -- 6.12.2 Radar configurations -- 6.12.3 Performance metric -- 6.12.4 Simulation results -- 6.12.5 Comments on the simulation results -- 6.13 Cognitive radar with single layer of memory -- 6.13.1 Cyclic directed information flow in cognitive radar with single layer of memory -- 6.13.2 Communication among subsystems in cognitive radar.

6.13.3 Communications between scene analyzer and perceptual memory.

A groundbreaking book from Simon Haykin, setting out the fundamental ideas and highlighting a range of future research directions.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

There are no comments on this title.

to post a comment.