Uncertain nonlinear algebraic solutions and their implementation using neural networks

In this article, we propose a dynamic recurrent approach to solve uncertain nonlinear algebraic equations. The approach is justified on the basis of net construction that recursively produces minimum neuron state energy which corresponds to the desired solution. Linearization via the Newton-Raphson method is employed in order to make the net converge to an appropriate region in the solution space. Some preliminary experimentation on non-trivial nonlinear examples are included and discussed. Approaches for hardware implementation of the recurrent dynamic neural network are presented. Comparison between a totally digital chip design and a hybrid analog/digital implementation utilizing MOSIS facilities is made. Evaluation and simulations on system, logic, and circuit levels are emphasized.