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A research on optimization of cellular neural networks based on evolutionary computation

基于进化计算的细胞神经网络优化研究

基本信息

批准号：
09680355
负责人：
NAGAO Tomoharu
金额：
$ 2.11万
依托单位：
TOKYO INSTITUTE OF TECHNOLOGY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1998
项目状态：
已结题

项目摘要

We developed a method to optimize neural networks named "Artificial Cellular Neural Networks (ACNN)". ACNN is a kind of neural network in which each neural unit connects only with his neighboring units. Since we need not connect any units which are far away from each other, we can easily make LSIs of two-dimensional and three-dimensional ACNN, In this research, connection weight among neural units is restricted to one of the three values, +1, -1 and 0, and every connection weights in ACNNs can be set independently. Therefore, we have to set many connection weights adequately in order to make an ACNN work well for a given task. We employed Genetic Algorithm(GA) for this optimization. Using a GA, we can make ACNNs for a variety of information processing tasks through generation iterations from the initial population of ACNNs which have randomly set connection weights. We developed automatic designing methods for 2D and 3D ACNN based on GA.Using our method, we can generate any ACNNs b.y defining evaluation functions appropriate for given tasks. As applications of our method, we treated several problems in the field of agent based artificial intelligence, First, we applied 2D ACNN for chasing problems. In these problems, action control of a virtual chaser was controlled by a 2D ACNN, Next, we applied 3D ACNN for a maze problem. The input and output of a 3D ACNN in this problem are the current neighbor of an agent in a maze and his next moving direction, respectively. By using our method, a 3D ACNN which moves the agent from the start point to the goal point in the shortest time is automatically generated. We developed the optimization method for 2D and 3D ACNN and applied them to artificial intelligence. We are now planning to make LSI chips of 2D and 3D ACNN.

我们开发了一种优化神经网络的方法，称为“人工细胞神经网络（ACNN）”。ACNN是一种神经网络，其中每个神经元只与其相邻的神经元连接。由于不需要连接任何相距较远的单元，因此可以很容易地构造二维和三维ACNN的最小二乘结构。在本研究中，神经元之间的连接权被限制为+1，-1和0三个值之一，并且ACNN中的每个连接权可以独立设置。因此，为了使ACNN能够很好地完成给定的任务，我们必须设置许多连接权重。我们采用遗传算法（GA）进行优化。使用遗传算法，我们可以使ACNN的各种信息处理任务，通过生成迭代从初始人口的ACNN随机设置连接权重。我们开发了基于遗传算法的二维和三维ACNN的自动设计方法。使用我们的方法，我们可以生成任何ACNN B.y定义适合给定任务的评价函数。作为该方法的应用，我们处理了基于Agent的人工智能领域中的几个问题。首先，我们将二维ACNN用于追踪问题。在这些问题中，一个虚拟追逐者的动作控制是由一个二维的ACNN控制的，接下来，我们将三维的ACNN应用于一个迷宫问题。在这个问题中，3D ACNN的输入和输出分别是迷宫中智能体的当前邻居和他的下一个移动方向。通过使用我们的方法，自动生成一个3D ACNN移动代理从起点到目标点在最短的时间。我们开发了二维和三维ACNN的优化方法，并将其应用于人工智能。我们现在计划制作2D和3D ACNN的LSI芯片。