Neuromorphic Solution of Nonlinear Two Point Boundary Value Problems

We present a general neuromorphic procedure for solving a class of dynamic programming problems, viz., Two-Point-Boundary-Value-Problems (TPBVP´s). The method consists of transformation of the TPBVP to the problem of minimization of an error function over a field of scalars. This error function is then mapped into the energy function of the Tank-Hopfield neural network leading to the synaptic interconnection weights and input bias currents which are adapted to the TPBVP to be solved. The method is illustrated by two examples.