Fuzzy inference neural network
Fuzzy inference neural network
复制标题
模糊推理神经网络
DOI：
10.1016/s0925-2312(96)00036-7
复制
发表时间：
1997
期刊：
影响因子：
6
通讯作者：
M. Hagiwara
中科院分区：
文献类型：
--
作者：
T. Nishima;M. Hagiwara
提出了一种新的模糊推理神经网络（FINN）设计模型。它能自动划分输入输出模式空间，并能从数值数据中提取模糊if-then规则。拟议的FINN是一个两层网络，利用Kohonen的算法。有三个学习阶段：自组织学习阶段，规则提取阶段和监督学习阶段。FINN具有以下特点：（1）前提部分的隶属函数构造在输入层和规则层之间;（2）具有自适应地选择合适数量的规则的能力;（3）能够提取更精细的模糊if-then规则。我们建议芬兰两个说明性的例子，无人驾驶汽车的模糊控制，股票价格的趋势预测。计算机仿真结果表明了该方法的有效性。
A new model for the design of Fuzzy Inference Neural Network (FINN) is proposed in this paper. It can automatically partition an input-output pattern space and can extract fuzzy if-then rules from numerical data. The proposed FINN is a two-layer network which utilizes Kohonen's algorithm. There are three learning phases: self-organizing learning phase, rule-extracting phase, and supervised learning phase. The FINN has the following distinctive features: (1) the membership functions of the premise part are constructed in the connection between the input layer and the rule layer; (2) it has an ability to select a suitable number of rules adaptively; and (3) it can extract more refined fuzzy if-then rules. We apply the proposed FINN to two illustrative examples, fuzzy control of an unmanned vehicle, and the prediction of the trend of stock prices. Computer simulation results indicate the effectiveness of the FINN.