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在CNAPS环境中实现脉冲耦合神经网络。

Implementation of pulse-coupled neural networks in a CNAPS environment.

作者信息

Kinser J M, Lindblad T

机构信息

Royal Institute of Technology, Department of Physics (Frescati), Stockholm S-104 05, Sweden.

出版信息

IEEE Trans Neural Netw. 1999;10(3):584-90. doi: 10.1109/72.761715.

Abstract

Pulse coupled neural networks (PCNN's) are biologically inspired algorithms very well suited for image/signal preprocessing. While several analog implementations are proposed we suggest a digital implementation in an existing environment, the connected network of adapted processors system (CNAPS). The reason for this is two fold. First, CNAPS is a commercially available chip which has been used for several neural-network implementations. Second, the PCNN is, in almost all applications, a very efficient component of a system requiring subsequent and additional processing. This may include gating, Fourier transforms, neural classifiers, data mining, etc, with or without feedback to the PCNN.

摘要

脉冲耦合神经网络(PCNN)是一种受生物启发的算法,非常适合图像/信号预处理。虽然已经提出了几种模拟实现方法,但我们建议在现有环境——适配处理器系统连接网络(CNAPS)中进行数字实现。这样做有两个原因。首先,CNAPS是一种商业可用芯片,已用于多种神经网络实现。其次,在几乎所有应用中,PCNN都是系统中一个非常高效的组件,需要后续的额外处理。这可能包括门控、傅里叶变换、神经分类器、数据挖掘等,无论是否有反馈给PCNN。

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