Adaptive Hopfield network

Abstract

This paper proposes an innovative enhancement of the classical Hopfield network algorithm (and potentially its stochastic derivatives) with an "adaptation mechanism" to guide the neural search process towards high-quality solutions for large-scale static optimization problems. Specifically, a novel methodology that employs gradient-descent in the error space to adapt weights and constraint weight parameters in order to guide the network dynamics towards solutions is formulated. In doing so, a creative algebraic approach to define error values for each neuron without knowing the desired output values for the same is adapted. © Springer-Verlag Berlin Heidelberg 2003.