Locally interconnected layered neural network for path optimization
Mohamad H. Hassoun, Ashvin J. Sanghvi · 1990
Highly interconnected networks of relatively simple processing elements are shown to be very effective in solving difficult optimization problems. Problems that fall into the broad category of finding a least-cost path between two points, given a distributed and sometimes complex cost map, are studied. A neurallike architecture and associated computational rules are proposed for the solution of this class of optimal path-finding problems in two- and higher-dimensional spaces. The proposed algorithm is local in nature and is very well suited for highly parallel, fine-grained, and distributed architectures. Also described is a collective multilayer neurallike architecture, characterized by speed of convergence, scalability, and guaranteed convergence to optimal solutions