摘要
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，从而具有更强的搜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例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．它可以用于求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型网络的局部极值问题．最后我们已将这种神经网络成功地应用于空间信号源的最大似然方向估计．
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.
出处
《电子学报》
EI
CAS
CSCD
北大核心
1998年第7期123-127,122,共6页
Acta Electronica Sinica
基金
国家攀登计划资助
国家自然科学基金
关键词
神经网络
暂态浊沌
时变增益
ML方向估计
Neural network, Transient chao, Time-variant gain, Nonlinear optimization , Chaotic annealing, ML direction extimtion
