Development and Applications of Learning Algorithms for Neural Networks

(1)It数学上研究了什么样的内部表示是可分离的。由三层前馈下一个神经网络的单个输出单元。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)We提出了一种新的递归传播学习算法。本文导出了一个生物学上合理的神经动力学模型，并利用随机过程的近似，得到了一个计算不动点的快速算法。并由此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，比较了两种方法的优缺点 ...更多信息 使用Backpropagation和Mean Field Networks进行当前传播。（3）联想记忆是通过网络的循环传播Teaming规则作为网络的平衡态来实现的。由于隐藏单元的数量可以不受限制地增加，所以信息容量可以足够大。这是所提出的网络与以前的网络相比，没有隐藏单元的主要优点。在我们的实验中，一些打印机字体用于记忆图案。数据表明，隐藏单元的数量越多，记忆率和正确联想率越高。(4)A本章提出了一种新的分组网络。虽然反向传播分组方法在许多应用中取得了成功，但仍存在一些困难，如局部极小问题、分组时间过长、不能逼近连续映射等。我们结合联合收割机的竞争学习和监督组队方法，以近似连续映射。仿真结果表明，所提出的分组算法的工作速度比反向传播方法，并具有可靠的收敛性能。少

(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