| 本文已被：浏览 4077次 下载 5743次 |
码上扫一扫！ |
|
|
| 多层反馈神经网络的FP学习和综合算法 |
|
张铃1,2, 张钹3,2
|
|
1.安徽大学人工智能研究所,合肥,230039;2.智能技术与系统国家重点实验室,北京,100084;3.清华大学计算机系,北京,100084
|
|
| 摘要: |
| 本文给出多层反馈神经网络的ＦＰ学习和综合算法，并讨论此类网络的性质，指出将它应用于聚类分析能给出不粒度的聚类，且具有收敛速度快（是样本个数的线性函数）、算法计算量少（是样本个数和输入、输出维数的双线性函数）、网络元件个数少、权系数简单（只取３个值）、网络容易硬件实现等优点．作为聚类器的神经网络的学习和综合问题已得到较圆满地解决. |
| 关键词: 多层反馈神经网络 学习算法 聚类 |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家攀登计划基金和国家自然科学基金资助. |
|
| A FORWARD PROPAGATION LEARNING ALGORITHM OF MULTILAYERED NEURAL NETWORKS WITH FEEDBACK CONNECTIONS |
|
ZHANG Ling,ZHANG Bo
|
| Abstract: |
| A forward propagation learning algorithm(FP)of multilayered neural networks with feedback connections is presented in this paper．And the properties of cluster networks are discussed．A cluster with different grain-sizes can be obtained by applying FP to cluster．Its convergent speed is just a linear function of sample size．Its computational complexity is a bilinear function of simple size and the dimension of imput vectors．The network constructed by the algorithm uses a comparatively fewer number of elements and its weight simply has one of three values，i．e．，一1，0，1．Thus，it can be easily implemented into electronic circuits．The authors also discuss the properties of the
network and show it is an ideal pattern classifier． |
| Key words: Multilayered neural network with feedback connections learning algorithm clustering |
