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REFERENCES[1] H. Al-Duwaish, M. N. Karim, and V. Chandrasekar, "Use of multilayer feedforward neural networks in identification and control of Wiener model,"Abstract-This note will address a linear minimum variance estimation of discrete-time systems with instantaneous and delayed measurements. Although the problem may be approached via system augmentation and standard Kalman filtering, the computation of filter may be expensive when the dimension of the system is high and the measurement lag is significant. In this note, a new tool, termed as reorganized innovation sequence, is presented for deriving the optimal filter. The optimal filter is given by two Riccati difference equations (RDEs) with the same dimension as that of the original system. The approach is shown to induce saving of computational cost as compared to the system augmentation approach, especially when the delay is large. Further, it can be extended to solving the more complicated fixed-lag smoothing in Krein space.

This paper is concerned with the infinite horizon linear quadratic optimal control for discrete‐time stochastic systems with both state and control‐dependent noise. Under assumptions of stabilization and exact observability, it is shown that the optimal control law and optimal value exist, and the properties of the associated discrete generalized algebraic Riccati equation (GARE) are also discussed. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

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