The authors present a novel nonlinear regulator design method that integrates linear optimal control techniques and nonlinear neural network learning methods. Multilayered neural networks are used to add nonlinear effects to the linear optimal regulator (LOR). The regulator can compensate for nonlinear system uncertainties that are not considered in the LOR design and can tolerate a wider range of uncertainties than the LOR alone. The salient feature of the regulator is that the control performance is much improved by using a priori knowledge of the plant dynamics as the system equation and the corresponding LOR. Computer simulations are performed to show the applicability and the limitations of the regulator.

The authors present a nonlinear regulator design method that uses multilayered neural networks (MNNs) for nonlinear systems operating in the presence of system uncertainties. The regulator consists of two MNNs for modeling and control in addition to the classical linear optimal regulator. The MNN for modeling is connected in parallel with the system equation to compensate for system uncertainties. The MNN for control is connected in parallel with the linear optimal regulator to compensate for the control errors caused by system uncertainties. Both MNNs are trained so that a desired response of the plant is obtained. A training signal of the MNN for control is acquired through the results of modeling. It is shown that the regulator can generate more sophisticated control inputs than the linear optimal regulator, even when the MNNs get stuck at local minima. A priori knowledge of the plant dynamics can be incorporated into the system equation and into the corresponding linear optimal regulator. The learning time is greatly reduced as compared with the case where MNN is used alone for modeling or control. Numerical simulations show that the regulator can stabilize a plant that is not stabilized by the linear regulator

In this paper, first, the normalized Triangular Lattice and Escalator algorithms for recursive least squares estimation are presented. Next, roundoff errors of both algorithms in fixed point arithmetic are analyzed and compared. Expressions are derived for the biases in the estimated coefficients due to roundoff errors. By improving the method of analysis, better expressions are obtained, as compared with the previous ones.

After a review of the statistical properties of multivariate autoregressive (MAR) spectral analysis method, we derive the asymptotic distributions of coherence and power contribution ratios obtained by MAR modeling. Then, using the results, we devise a statistical test about the causality relations between variates. Finally we apply the test to simulation and actual data to see the validity of the present analysis.

A failure diagnosis model which consists of a set of units that are faulty, a set of measurements to detect a faulty unit, and an incidence matrix representing the binary relation between these two sets is presented. A binary relation is defined by whether or not a fault of one unit is detected by a measurement. Considering this failure diagnosis model as a simplicial complex in topological geometry, the relationship between the diagnostic aspects of the model and the topological properties of the corresponding simplicial complex is studied. The diagnosability of a failure diagnosis model with multiple faults is expressed as a covering property of each simplex in a simplicial complex. The capability of distinguishing a faulty unit by a given number of measurement is determined by examining the global connective structure of the simplicial complex. This analysis of connectivity is done by performingq-analysis on a simplicial complex. It is noted hat all possible combinations of fault patterns are considered under a permissible number of faults. However, in actual systems, only restricted fault patterns are possible in which faulty units are functionally connected with each other, even in multiple fault situations.

Optimal linear coding schemes for feedback communication with noisy side information are derived. The performance of two schemes with side information are compared with the information-theoretic bounds. The side information at the transmitter Significantly reduces the mean-squared error. The corresponding continuous-time system is briefly mentioned.

A feedback communication system which has noisy side information at the receiver is considered. The estimation scheme is a simple extension of the conventional one without side information. The resulting mean-squared error is compared with the rate-distortion bound obtained recently by Wyner and Ziv.