Mamalian-like neural networks for dynamic information processing and its learning algorithm

用于动态信息处理的类哺乳动物神经网络及其学习算法

基本信息

批准号：
04805032
负责人：
TAKAHASHI Haruhisa
金额：
$ 1.28万
依托单位：
The University of Electro-Communications
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1992
资助国家：
日本
起止时间：
1992 至 1993
项目状态：
已结题

项目摘要

(1)It is mathematically investigated as to what kind of internal representations are separable by a single output unit of a three layr feednext neural network. A topologically described necessary and sufficient condition is shown for partitions of input spaces to be classified by the output unit. Then an efficient algorithm is proposed for checking if a given partition of the input space is resulted in linear separation at the output unit.(2)(3)These papers improves the sample complexity needed for reliable generalization in the PAC learnability in machine learning. By introducing an ill-posed learning algorithm which gives error worse over the candidates of network realizarions that are attained by minimizing empirical error, we can refine the order of the sample complexity, whereas the previous methods seek the uniform error over the whole configuration space. Essential VC dimension of concept classes, which is smaller than or equal to the number of modifiable system parameters, is introduced for calculating the generalization error instead of the traditional VC dimension analysis. Noisy learning is also treated.(4)In this paper we propose a very simple recurrent neural network(VSRN)architecture which is a three-layr network and contains only self-loop recurrent connections in the hidden layr. The role of the recurrent connection is explained by the network dynamic and its function will be acquired by learning from finite examples like a mamalian action. Through the learning process some characteristic functions observed in the mamalian auditory systems are found automatically acquired by the network. These contain on-neuron, off-neuron and on-off-neuron. This architecture can perform phoneme spotting in real time by utilizing these characteristic functions. Some simulation experiments are done to investigate the recognition performance.

（1）在数学上研究了哪种内部表示可以通过三个Layr FeedNext神经网络的单个输出单元分离。为要由输出单元分类的输入空间的分区显示了拓扑描述的必要条件。然后提出了一种有效的算法，用于检查输入空间的给定分区是否导致输出单元处的线性分离。（2）（3）这些论文提高了在机器学习中PAC可靠性的可靠概括所需的样本复杂性。通过引入一种不适的学习算法，该算法使网络实现的候选者的错误更糟，这些算法通过最大程度地减少经验误差而实现的候选者，我们可以完善样本复杂性的顺序，而先前的方法在整个配置空间中寻求统一的误差。引入了用于计算概括误差而不是传统的VC维度分析的概念类的基本VC维度，该概念类别小于或等于可修改系统参数的数量。（4）在本文中，我们提出了一个非常简单的复发性神经网络（VSRN）体系结构，该架构是一个三层网络，仅包含隐藏层中的自动循环连接。复发连接的作用是通过网络动态来解释的，其功能将通过从有限示例（如Mamalian动作）中学习来获取。通过学习过程，在Mamalian听觉系统中观察到的一些特征功能被网络自动获取。这些包含在神经上，神经元和on-off-neuron。该体系结构可以通过利用这些特征功能来实时执行音素点。进行了一些模拟实验来研究识别性能。