并行、自组织、一致性神经网络
Parallel, Self Organizing, Consensus Neural Networks
摘要：
提出了一种新的神经网络结构（PSCNN），以提高这类网络的性能和速度。该体系结构具有自组织等优点，并具有输入并行性和基于一致性的决策等优越特性。由于这个网络的属性，它被研究了在并行处理器（Ncube机）以及常规时序机上的实现。架构以最大化性能的方式自组织其自己的模块。由于它是完全并行的，所以回忆和学习过程都非常快。该网络的性能进行了比较，反向传播网络的语言感知，遥感和二进制逻辑（异或）的问题。PSCNN表现出优越的性能，在所有的情况下研究。
英文摘要：
A new neural network architecture (PSCNN) is developed to improve performance and speed of such networks. The architecture has all the advantages of the previous models such as self-organization and possesses some other superior characteristics such as input parallelism and decision making based on consensus. Due to the properties of this network, it was studied with respect to implementation on a Parallel Processor (Ncube Machine) as well as a regular sequential machine. The architecture self organizes its own modules in a way to maximize performance. Since it is completely parallel, both recall and learning procedures are very fast. The performance of the network was compared to the Backpropagation networks in problems of language perception, remote sensing and binary logic (Exclusive-Or). PSCNN showed superior performance in all cases studied.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
