Improved chaotic neuro-computer with output-coding for quadratic assignment problems

In this paper, we improve performance of a chaotic neuro-computer in solving quadratic assignment problems (QAPs) by adopting an output-coding which constructs a feasible solution from analog internal-states of neurons at each iteration. Through measurements from the chaotic neuro-computer hardware, we show that we constantly obtain the optimum solution for size-10 QAPs. Furthermore, chaotic search dynamics through chaotic itinerancy is confirmed from time evolutions of a cost function and an energy function. Moreover, we observe internal states of arbitrary three neurons in a network to extract useful information on network dynamics that is effective in solving the QAPs.