A Synthesis Theory of Stable Equilibrium Solutions for Large Scale Dynamical Neural Networks and Its Application to Associative Memories.

大规模动态神经网络稳定平衡解的综合理论及其在联想记忆中的应用。

基本信息

批准号：
07650464
负责人：
INABA Hiroshi
金额：
$ 1.47万
依托单位：
Tokyo Denki University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1996
项目状态：
已结题

项目摘要

In this research project, a synthesis theory for dynamical neural networks using the McCullough-Pitts model proposed in 1943 as a simple mathematical model for a brain neuron was studied particularly focussing on their stable equilibrium solutions. In particular, having in mind application of neural networks to associative memories, first a dynamical neural network having a special structure, called a module neural network, was introduced to store primitive information, and its basic behaviors were investigated. Then, by connecting a number of such module neural networks a large scale and its equilibrium solutions were studied.The main results obtained are listed below :1.A method for constructing a module neural network having a given set of vectors as its stable equilibrium solutions, and further a possibility of ajusting the domain of a stable equilibrium solution was discussed.2.A module dynamical neural network, having a special structure, was introduced, and then a method was proposed for constructing a large scale neural network, called a multi-module neural network, by connecting a number of such module neural networks without changing all the equilibrium solutions of the connectied module neural networks.3.To avoid the rapid decrease in the ability of associative memories due to the number of information vectors to be stored approaching the dimension of the information vectors, a generalized dynamical neural network was proposed, and its construction method was discussed.4.A number of computer simulations were performed to evaluate the effectiveness of the theoretical results obtained.

在该研究项目中，研究了一种针对1943年提出的McCullough-Pitts模型作为脑神经元的简单数学模型的综合理论，特别专注于其稳定的平衡溶液。特别是，考虑到将神经网络应用于关联记忆，首先引入了具有特殊结构的动态神经网络，称为模块神经网络，并研究了其基本行为。 Then, by connecting a number of such module neural networks a large scale and its equilibrium solutions were studied.The main results obtained are listed below :1.A method for constructing a module neural network having a given set of vectors as its stable equilibrium solutions, and further a possibility of ajusting the domain of a stable equilibrium solution was discussed.2.A module dynamical neural network, having a special structure, was introduced, and then提出了一种方法来构建大规模的神经网络，称为多模型神经网络，通过连接许多此类模块神经网络而不改变连接模块神经网络的所有均衡解决方案，避免避免迅速降低信息网络的能力，因为信息媒介的数量是构建的动态范围，并构建了一个通用的方法，是一种通用的方法，是一种通用的方法，是一种通用的范围，是一种通用的方法，是一种通用的范围，是一种通用的范围。讨论了。4。进行了数量的计算机模拟，以评估获得的理论结果的有效性。