A Chaotic Annealing Neural Network with Gain Sharpening and Its Application to the 0/1 Knapsack Problem

摘要

In this article we present a modified transiently chaotic neural network model and then use it to solve the 0/1 knapsack problem. During the chaotic searching the gain of the neurons is gradually sharpened, this strategy can accelerate the convergence of the network to a binary state and keep the satisfaction of the constraints. The simulation demonstrates that the approach is efficient both in approximating the global solution and the number of iterations.