The multi-zero artificial neural network was derived from a study of the stability and convergence properties of a feedback (or auto-associative) neural system. The nonlinear response function of neurons in the system is an odd polynomial (or a topologically similar) function of 2M $PLU 1 zeros with odd zeros equal to a set of consecutive integers. If the connection matrix is programmed correctly, the system will then perform stable operations exhibiting the following characteristics: (1) The system will transform any N-bit analog input to an N-bit, M-ary (or M-valued), digital output. (2) The output will be locked in when the input is removed. It will be changed to another locked in digital vector when it receives another input. (3) The speed is fast because the circuit is free-running, parallel, and M-ary. The accuracy is high because the computation is digital. Because of these unique properties, the network can be used in the design of a fast computing system. This paper reports the origin of this multizero system, the analysis of its properties, and the design of a fast, M-ary, digital multiplier using this system.
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机译:从反馈(或自动关联)神经系统的稳定性和收敛性质的研究导出了多零人工神经网络。系统中神经元的非线性响应函数是奇数多项式(或拓扑上类似的)函数的2M $ PLU 1零,奇数零等于一组连续的整数。如果连接矩阵正确编程,则系统将执行稳定的操作,呈现以下特征:(1)系统将任何N位模拟输入转换为n位,m-ary(或m-valued),数字输出。 (2)拆除输入时,输出将锁定。当它收到另一个输入时,它将被改为在数字矢量中锁定。 (3)速度快,因为电路是自由运行的,并行和M-ARY。精度高,因为计算是数字的。由于这些独特的属性,网络可以用于设计快速计算系统的设计。本文报告了该MultiRiredo系统的起源,其性能分析以及使用该系统的快速,M-ARY,数字乘法器的设计。
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