Stochastic Systems: Estimation, Identification, and Adaptive Control

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[1]  N. Wiener The Wiener RMS (Root Mean Square) Error Criterion in Filter Design and Prediction , 1949 .

[2]  R. Strauch Negative Dynamic Programming , 1966 .

[3]  D. Ornstein On the existence of stationary optimal strategies , 1969 .

[4]  A. F. Veinott Discrete Dynamic Programming with Sensitive Discount Optimality Criteria , 1969 .

[5]  T. Kailath The innovations approach to detection and estimation theory , 1970 .

[6]  K. Åström Introduction to Stochastic Control Theory , 1970 .

[7]  Václav Peterka,et al.  On steady state minimum variance control strategy , 1972, Kybernetika.

[8]  M. Schäl Dynamic programming under continuity and compactness assumptions , 1973, Advances in Applied Probability.

[9]  Thomas Kailath,et al.  A view of three decades of linear filtering theory , 1974, IEEE Trans. Inf. Theory.

[10]  W. Stout Almost sure convergence , 1974 .

[11]  A. Lindquist A New Algorithm for Optimal Filtering of Discrete-Time Stationary Processes , 1974 .

[12]  M. Morf Fast Algorithms for Multivariable Systems , 1974 .

[13]  G. Bierman Measurement updating using the U-D factorization. [for Kalman matrix filter error covariance] , 1975 .

[14]  Manfred SchÄl,et al.  Conditions for optimality in dynamic programming and for the limit of n-stage optimal policies to be optimal , 1975 .

[15]  M. Morf,et al.  Square-root algorithms for least-squares estimation , 1975 .

[16]  G. Bierman Factorization methods for discrete sequential estimation , 1977 .

[17]  Patrick Dewilde,et al.  Schur recursions, error formulas, and convergence of rational estimators for stationary stochastic sequences , 1981, IEEE Trans. Inf. Theory.

[18]  Gérard Favier,et al.  Filtrage, modélisation et identification de systèmes linéaires stochastiques à temps discret , 1982 .

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

[20]  A. Papoulis Levinson’s Algorithm, Wold’s Decomposition, and Spectral Estimation , 1985 .

[21]  P. Kumar,et al.  Minimum variance control of discrete time multivariable Armax systems , 1986 .