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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)It的数学研究，什么样的内部表示是可分离的一个单一的输出单元的三层前馈神经网络。一个拓扑描述的必要和充分条件示出的分区的输入空间被分类的输出单元。然后提出了一种有效的算法，用于检查输入空间的给定划分是否导致输出单元的线性分离。（2）（3）这些论文提高了机器学习中PAC可学习性的可靠泛化所需的样本复杂度。通过引入一个不适定的学习算法，使错误更糟糕的候选人的网络实现，通过最小化经验误差，我们可以细化的顺序的样本复杂性，而以前的方法寻求在整个配置空间的均匀误差。引入概念类的本质VC维（小于或等于系统可修改参数的个数）代替传统的VC维分析，用于计算泛化误差。噪声学习也被处理。(4)In本文提出了一种非常简单的递归神经网络（VSRN）结构，它是一个三层网络，在隐层中只包含自循环递归连接。递归连接的作用由网络动力学解释，它的功能将通过学习从有限的例子，像一个哺乳动物的行动。通过学习过程，发现了一些在哺乳动物听觉系统中观察到的特征函数。其中包括on-neuron、off-neuron和on-off-neuron。该结构可以通过利用这些特征函数来执行真实的音素定位。通过仿真实验研究了该方法的识别性能。