Kalman Filtering and Neural Networks

From the Publisher: Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear. The book deals with important applications in such fields as control, financial forecasting, and idle speed control.

[1]  W. Torgerson.  Multidimensional scaling: I. Theory and method , 1952 .

[2]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[3]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[4]  H. Rauch.  Solutions to the linear smoothing problem , 1963 .

[5]  R. Kopp,et al.  LINEAR REGRESSION APPLIED TO SYSTEM IDENTIFICATION FOR ADAPTIVE CONTROL SYSTEMS , 1963 .

[6]  Henry Cox,et al.  On the estimation of state variables and parameters for noisy dynamic systems , 1964 .

[7]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[8]  L. Baum,et al.  An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology , 1967 .

[9]  D. Mayne,et al.  Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering† , 1969 .

[10]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[11]  R. K. Mehra,et al.  Identification of stochastic linear dynamic systems using Kalman filter representation , 1971 .

[12]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[13]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[14]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[15]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[16]  R. Mañé,et al.  On the dimension of the compact invariant sets of certain non-linear maps , 1981 .

[17]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[18]  Chan‐Fu Chen,et al.  The EM Approach to the Multiple Indicators and Multiple Causes Model via the Estimation of the Latent Variable , 1981 .

[19]  R. Shumway,et al.  AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM , 1982 .

[20]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[21]  P. Kumar,et al.  Theory and practice of recursive identification , 1985, IEEE Transactions on Automatic Control.

[22]  A. Lapedes,et al.  Nonlinear signal processing using neural networks: Prediction and system modelling , 1987 .

[23]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[24]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[25]  D. Rubin.  Using the SIR algorithm to simulate posterior distributions , 1988 .

[26]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[27]  Andrew M. Fraser,et al.  Information and entropy in strange attractors , 1989, IEEE Trans. Inf. Theory.

[28]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[29]  J. Hull.  Options, Futures, and Other Derivatives , 1989 .

[30]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[31]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[32]  S. Haykin,et al.  Is there a radar clutter attractor , 1990 .

[33]  Randal Brumbaugh,et al.  An aircraft model for the AIAA Controls Design Challenge , 1991 .

[34]  Simon Haykin,et al.  Adaptive filter theory (2nd ed.) , 1991 .

[35]  Lee A. Feldkamp,et al.  Decoupled extended Kalman filter training of feedforward layered networks , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[36]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[37]  Eric P. Fox.  Bayesian Statistics 3 , 1991 .

[38]  Generalized dimensions of laser attractors. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[39]  Michael A. West.  Mixture Models, Monte Carlo, Bayesian Updating and Dynamic Models , 1992 .

[40]  Alan E. Gelfand,et al.  Bayesian statistics without tears: A sampling-resampling perspective , 1992 .

[41]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[42]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[43]  H. Leung,et al.  Chaotic radar signal processing over the sea , 1993 .

[44]  John H. L. Hansen,et al.  Discrete-Time Processing of Speech Signals , 1993 .

[45]  John Moody,et al.  Predicting the U.S. Index of Industrial Production (Extended Abstract) , 1993 .

[46]  M. West.  Approximating posterior distributions by mixtures , 1993 .

[47]  Lee A. Feldkamp,et al.  Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.

[48]  T. Kailath,et al.  A state-space approach to adaptive RLS filtering , 1994, IEEE Signal Processing Magazine.

[49]  Jun S. Liu,et al.  Sequential Imputations and Bayesian Missing Data Problems , 1994 .

[50]  James P. Dutton.  Development of a Nonlinear Simulation for the McDonnell Douglas F-15 Eagle with a Longitudinal TECS Control-Law. , 1994 .

[51]  R. Kohn,et al.  On Gibbs sampling for state space models , 1994 .

[52]  Andreas S. Weigend,et al.  Paradigm change in prediction , 1994, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

[53]  Takens,et al.  Estimation of the dimension of a noisy attractor. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[54]  S. Frühwirth-Schnatter.  Bayesian Model Discrimination and Bayes Factors for Linear Gaussian State Space Models , 1995 .

[55]  Simon Haykin,et al.  Detection of signals in chaos , 1995, Proc. IEEE.

[56]  D. Avitzour.  Stochastic simulation Bayesian approach to multitarget tracking , 1995 .

[57]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[58]  Henry D. I. Abarbanel,et al.  Analysis of Observed Chaotic Data , 1995 .

[59]  Jun S. Liu,et al.  Blind Deconvolution via Sequential Imputations , 1995 .

[60]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[61]  Geoffrey E. Hinton,et al.  The EM algorithm for mixtures of factor analyzers , 1996 .

[62]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[63]  David J. C. MacKay,et al.  BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION , 1996 .

[64]  Geoffrey E. Hinton,et al.  Parameter estimation for linear dynamical systems , 1996 .

[65]  S. Julier,et al.  A General Method for Approximating Nonlinear Transformations of Probability Distributions , 1996 .

[66]  Steve Rogers,et al.  Adaptive Filter Theory , 1996 .

[67]  David Lowe,et al.  Practical methods of tracking of nonstationary time series applied to real-world data , 1996, Defense + Commercial Sensing.

[68]  Eric A. Wan,et al.  Dual Kalman Filtering Methods for Nonlinear Prediction, Smoothing and Estimation , 1996, NIPS.

[69]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[70]  Geoffrey E. Hinton,et al.  Switching State-Space Models , 1996 .

[71]  Mahesan Niranjan.  Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches , 1996, NIPS.

[72]  Geoffrey E. Hinton,et al.  Modeling the manifolds of images of handwritten digits , 1997, IEEE Trans. Neural Networks.

[73]  T. Higuchi.  Monte carlo filter using the genetic algorithm operators , 1997 .

[74]  Eric A. Wan,et al.  Neural dual extended Kalman filtering: applications in speech enhancement and monaural blind signal separation , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[75]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[76]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[77]  P. Djurić,et al.  A fast-weighted Bayesian bootstrap filter for nonlinear model state estimation , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[78]  S. Haykin,et al.  Chaotic dynamics of sea clutter. , 1999, Chaos.

[79]  Lee A. Feldkamp,et al.  Extensions and enhancements of decoupled extended Kalman filter training , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[80]  Simon J. Godsill,et al.  On sequential simulation-based methods for Bayesian filtering , 1998 .

[81]  Nicol N. Schraudolph,et al.  Online Local Gain Adaptation for Multi-Layer Perceptrons , 1998 .

[82]  Zoubin Ghahramani,et al.  Learning Nonlinear Dynamical Systems Using an EM Algorithm , 1998, NIPS.

[83]  Arnaud Doucet,et al.  Sequential Monte Carlo methods for optimisation of neural network models , 1998 .

[84]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[85]  M. Nørgaard,et al.  Advances in Derivative-Free State Estimation for Nonlinear Systems , 1998 .

[86]  Radford M. Neal.  Assessing Relevance determination methods using DELVE , 1998 .

[87]  Volker Tresp,et al.  Fisher Scoring and a Mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models , 1998, NIPS.

[88]  Eric A. Wan,et al.  A two-observation Kalman framework for maximum-likelihood modeling of noisy time series , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[89]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[90]  Eric A. Wan,et al.  Removal of noise from speech using the dual EKF algorithm , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[91]  Kevin Murphy,et al.  Switching Kalman Filters , 1998 .

[92]  Christopher M. Bishop,et al.  Neural networks and machine learning , 1998 .

[93]  Zoubin Ghahramani,et al.  A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.

[94]  Rudolph van der Merwe,et al.  Dual Estimation and the Unscented Transformation , 1999, NIPS.

[95]  Michael E. Tipping.  The Relevance Vector Machine , 1999, NIPS.

[96]  Andrew H. Gee,et al.  Nonlinear State Space Estimation With Neural Networks And The Em Algorithm , 1999 .

[97]  Gomes de Freitas,et al.  Bayesian methods for neural networks , 2000 .

[98]  Bernie Mulgrew,et al.  False detection of chaotic behaviour in the stochastic compound k-distribution model of radar sea clutter , 2000, Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496).

[99]  Geoffrey E. Hinton,et al.  Variational Learning for Switching State-Space Models , 2000, Neural Computation.

[100]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[101]  Eric A. Wan,et al.  Nonlinear estimation and modeling of noisy time series by dual kalman filtering methods , 2000 .

[102]  Kazufumi Ito,et al.  Gaussian filters for nonlinear filtering problems , 2000, IEEE Trans. Autom. Control..

[103]  Arnaud Doucet,et al.  Sequential Monte Carlo Methods to Train Neural Network Models , 2000, Neural Computation.

[104]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[105]  Zoubin Ghahramani,et al.  Propagation Algorithms for Variational Bayesian Learning , 2000, NIPS.

[106]  Nando de Freitas,et al.  An Introduction to Sequential Monte Carlo Methods , 2001, Sequential Monte Carlo Methods in Practice.

[107]  Rudolph van der Merwe,et al.  Efficient derivative-free Kalman filters for online learning , 2001, ESANN.

[108]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[109]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[110]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[111]  Simon J. Julier,et al.  The scaled unscented transformation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[112]  Simon Haykin,et al.  Uncovering nonlinear dynamics-the case study of sea clutter , 2002, Proc. IEEE.

[113]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .