title: A biologically inspired neural network for dynamic programming.
abstract: An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.
authors.affiliation: ICMC, University of São Paulo, Av. Trabalhador Sancarlense, 400,São Carlos, São Paulo 13560-970, Brasil. rafrance@icmsc.sc.usp.br
authors.forename: R A
authors.initials: RA
authors.lastname: Francelin Romero
authors.forename: J
authors.initials: J
authors.lastname: Kacpryzk
authors.forename: F
authors.initials: F
authors.lastname: Gomide
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D001330:Electronic Data Processing; D017143:Fuzzy Logic; D008959:Models, Neurological; D016571:Neural Networks, Computer; D020557:Neurolinguistic Programming; D017711:Nonlinear Dynamics; D013269:Stochastic Processes; D013569:Synapses