一种具有暂态混沌和时变增益的神经网络及其在优化计算中的应用

本文提出了一种具有暂态混沌和时变增益的神经网络。通过引入暂态混沌和时变增益，该网络比Ｈｏｐｆｉｅｌｄ型网络具有更加丰富和更为灵活的动力学特征，从而具有更强的插索全局最优解或近似全局最优解的能力。网络经过一个短暂的倒分叉过程逐渐趋近一个常规的Ｈｏｐｆｉｅｌｄ神经网络，并为其提供了一个在全局最优解附近的初值。它可以用于救解各种复杂的优化问题。大量的数字模拟表明网络能很好地解决Ｈｏｐｆｉｅｌｄ型网络的局

Neural Networks with Transient Chaos and Time-variant Gain and Its Application to Optimization Computations

In this article a neural network model with transient chaos and time-variant gain is proposed.By introducing transiently chaos and time-valiant gain, the proposed neural neira; metwork has richer and more flexible dynamics rather than Hopfield-like neural networks only with POint attractors, so that it can be expected to have higher ability of searching for globally optimal or near-optimal solutions. After going through an inversebifurcation process, the neural network gradually approaches to a conventional Hopfield netal network stalting from a good initial state. It can be used to solving various complicated optimization problem and associative memories. Extensive numerical simlilations show that the network would not be stuck into local minima. Finally,we applied the network to maximum likelihood direction estimation of spatial signal sources.