ART1和ART2人工神经网络在环形和网状结构上的实现

G. D. Ghare, L. Patnaik
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引用次数: 2

摘要

人工神经网络(ann)正被用于解决模式识别、机器人控制、VLSI CAD等领域的各种问题。在大多数这些应用中,人工神经网络的快速响应是必不可少的。然而,人工神经网络由大量的人工神经元组成,它们之间是一个庞大的互连网络。因此,这些人工神经网络的实现涉及执行计算机密集型操作。因此,多处理器系统的使用是必要的。在本文中,我们介绍了在环形和网状结构上实现ART1和ART2人工神经网络。介绍了系统的总体设计和实现。介绍了该算法在环形网格、二维网格和n维网格拓扑结构上的性能。为实现ART1而提出的并行算法并不特定于任何特定的体系结构。ARTE的并行算法更适合于环形结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of ART1 and ART2 Artificial Neural Networks on Ring and Mesh Architectures
The Artificial Neural Networks (ANNs) are being used to solve a variety of problems in pattern recognition, robotic control, VLSI CAD and other areas. In most of these applications, a speedy response from the ANNs is imperative. However, ANNs comprise a large number of artificial neurons, and a massive interconnection network among them. Hence, implementation of these ANNs involves execution of computer-intensive operations. The usage of multiprocessor systems therefore becomes necessary. In this article, we have presented the implementation of ART1 and ART2 ANNs on ring and mesh architectures. The overall system design and implementation aspects are presented. The performance of the algorithm on ring, 2-dimensional mesh and n-dimensional mesh topologies is presented. The parallel algorithm presented for implementation of ART1 is not specific to any particular architecture. The parallel algorithm for ARTE is more suitable for a ring architecture.
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