Neural Networks for Synthesis and Optimization of Antenna Arrays

This paper describes a usual application of back-propagation neural networks for synthesis and optimization of antenna array. The neural network is able to model and to optimize the antennas arrays, by acting on radioelectric or geometric parameters and by taking into account predetermined general criteria. The neural network allows not only establishing important analytical equations for the optimization step, but also a great flexibility between the system parameters in input and output. This step of optimization becomes then possible due to the explicit relation given by the neural network. According to different formulations of the synthesis problem such as acting on the feed law (amplitude and/or phase) and/or space position of the radiating sources, results on antennas arrays synthesis and optimization by neural networks are presented and discussed. However ANN is able to generate very fast the results of synthesis comparing to other approaches

Input Frequencies Optimization Based on Genetic Algorithm for Maximal Mutual Information

Among encountered problems in digital and analog communications, there is mismatch between canals and sources. As regards theory of information, unfortunately, this mismatch found expression in information loss during transfer to reception side. In order to settle the problem, the solution consists in adjustment of probability law at source so that we maximize the mean mutual information. For a little number of symbols, either at emission or at reception, the work can be done analytically with some difficulties. Unfortunately, the problem have tendency to become more and more difficult and complicated as number of symbols increases. In this case and as alternative, we propose a non-traditional optimization method, namely genetic algorithm, which will express, with regard to our problem, all its efficiency through this paper with some conclusive applications