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Development and Applications of Learning Algorithms for Neural Networks

神经网络学习算法的开发和应用

基本信息

批准号：
02650235
负责人：
TAKAHASHI Haruhisa
金额：
$ 1.41万
依托单位：
The University of Elector-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：
1990
资助国家：
日本
起止时间：
1990 至 1991
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-02650235/
关键词：
Neural network Learning Algorithm Back Propagation Recurrent Propagation 線型分離線型分離可能性

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 layer feed next 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)We propose in this paper a new recurrent propagation learning algorithm. A biologically plausible neurodynamics is derived from which a quick algorithm to compute fixed points is obtained applying an approximation of stochastic process. The sensitivity of the networks is also obtained and from that a new recurrent propagation teaming algorithm is proposed. Our algorithm can run 10 times more quick over the previous ones. Furthermore, some unstable problems in recurrent propagation are overcome. Some simulation results are given to compare the re … More current propagations with Backpropagation and Mean Field Networks.(3)Associative memory is realized by the, recurrent propagation Teaming rule as equilibrium states ; of the network. Since the number of hidden units can be increased unrestrictedly, the information capacity can be sufficiently large. This is the main advantage of the proposed network compared with previous ones that have no hidden units. Some printer fonts are used for patterns to be memorized in our experiments. The data show that the more the number of hidden units, the more the rate of memorization and correct association.(4)A new teaming network is proposed in this chapter. Although the Backpropagation Teaming method has been succeeded in many applications, some difficulties exists, such as the local minimal problem, too long Teaming Teaming times, and capability of approximation to continuous mappings. We combine the competitive learning and supervised teaming methods in order to approximate continuous mappings. Some simulation results show that the proposed teaming algorithm works extremely more quick than the Back Propagation method and has reliable convergence property. Less

(1)从数学上研究什么样的内部表示是可分离的。由单个输出单元组成的三层反馈神经网络。给出了输入空间划分按输出单位分类的一个拓扑学描述的充要条件。然后提出了一种有效的算法来检验给定的输入空间划分是否导致输出单元的线性分离。(2)本文提出了一种新的递归传播学习算法。导出了一种生物上可信的神经动力学，并利用随机过程的近似得到了计算不动点的快速算法。在此基础上，提出了一种新的递归传播分组算法。我们的算法可以比以前的算法快10倍。此外，还克服了递归传播中的一些不稳定问题。给出了一些仿真结果，比较了Re-…使用反向传播和平均场网络实现更多的电流传播。(3)联想记忆是通过循环传播分组规则实现的，作为网络的平衡状态。由于可以不受限制地增加隐藏单元的数量，因此信息容量可以足够大。与以前没有隐藏单元的网络相比，这是所提议的网络的主要优势。在我们的实验中，一些打印机字体用于记忆图案。数据表明，隐含单元的数量越多，记忆和正确联想的速度就越快。(4)提出了一种新的分组网络。尽管反向传播分组方法已经在许多应用中获得了成功，但仍然存在一些困难，如局部极小问题、分组时间过长以及对连续映射的逼近能力。我们将竞争学习和有监督分组方法结合起来，以逼近连续映射。仿真结果表明，该分组算法比反向传播算法具有更快的收敛速度和可靠的收敛特性。较少