摘要
A Hopfield-type neural network with adaptively changing synaptic weights and activation function parameters is presented to solve unconstrained nonlinear programming problems. The network performance is similar to that of the trust region method in the mathematical programming literature. There is a sub-network to solve quadratic programming problems with simple upper and lower bounds. By sequentially activating the sub-network under the control of an externul computer or a special analog or digital processor that adjusts the weights and parameters,the network solves a sequence of unconstrained nonlinear programming problems. Convergence proof and numerical results are given.
A Hopfield-type neural network with adaptively changing synaptic weights and activation function parameters is presented to solve unconstrained nonlinear programming problems. The network performance is similar to that of the trust region method in the mathematical programming literature. There is a sub-network to solve quadratic programming problems with simple upper and lower bounds. By sequentially activating the sub-network under the control of an externul computer or a special analog or digital processor that adjusts the weights and parameters,the network solves a sequence of unconstrained nonlinear programming problems. Convergence proof and numerical results are given.
