For pt.II. see ibid., p. 490-501. The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storag...

In Multi-Input Multi-Output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N -dimensional complex space. In general, this algorithm is known to be NP hard. In this paper, we propose a quasi-maximum likelihood algorithm based on Semi-Definite Programming (SDP). We introduce several SDP relaxation models for MIMO systems, with increasing ...

We are using Gaussian Mixture Models (GMM) as a tool to construct local mappings of nonlinear Multi-Input Multi-Output (MIMO) systems. In this work we combine the advantages of GMM with the Kalman filter. To improve the accuracy of the local linear mappings in a potentially large dimensional state space, we propose to initialize the GMM parameters with Vector Quantization (VQ) or its more parsi...

The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical method...

One of the options to mitigate fading effects in mobile communication is adaptive transmitter gain. This paper proposes a simple technique to eliminate the effects of fading in different multi antenna set-ups by varying the transmitter gain. The multi antenna set-ups considered in this paper include Single input-single output (SISO), Single input-multi output (SIMO), Multi input-single output (...

A generalized formulation to design Multi-Input-Multi-Output (MIMO) compliant mechanisms is presented in this work. This formulation also covers the simplified cases of the design of Multi-Input and Multi-Output compliant mechanisms, more commonly used in the literature. A Sequential Element Rejection and Admission (SERA) method is used to obtain the optimum design that converts one or more inp...

In this paper, direct adaptive neural network (NN) control is developed for a class of multi-input and multi-output (MIMO) nonlinear systems in discrete-time. To solve the difficulty of nonaffine appearance of control, implicit function theorem is exploited to assert the existence of an ideal desired feedback control (IDFC). Then, high-order-neural-network (HONN) is employed to approximate the ...

A control strategy for stabilizing multi-input multi-output (MIMO) linear systems via feedback of the output and its derivatives is introduced. The feedback law is only based on structural properties of the system such as vector relative degree, stable zero dynamics and a positive definite high frequency gain matrix.

In this article, we address the problem of robust waveform optimization for improving the worst-case detection performance of multi-input multi-output (MIMO) space–time adaptive processing (STAP) in the presence of colored Gaussian disturbance. A novel diagonal loading-based method is proposed to optimize the waveform covariance matrix for maximizing the worst-case output signal-interference-no...

The open problem of the existence of double coprime factorizations (DCFs) for multi-input/multi-output (MIMO) multidimensional (nD) linear systems is considered in this paper. It is shown that DCFs exist for a class of MIMO nD linear systems. A simple and efficient method is proposed for the construction of DCFs. The main result of the paper is to show how to construct a coprime (over the ring ...