Feedback Nerual Network(五):Design CHNN

本文探讨了CHNN(连续Hopfield神经网络)与DHNN(离散Hopfield神经网络)之间的区别。主要关注两者在结构与工作模式上的不同,包括它们如何通过求解非线性微分方程组来实现状态转移,以及稳定性的定义方式。

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Difference between CHNN & DHNN:
First-order differential equation corresponds to first-order inertial link惯性环节. For DHNN, this formula is yi=si.
Usually it is not Binary Function any more here, but use  S-type Function or Hyperbolic Function.

 

CHNN structure:

Network Structure & Work Mode
Given an initial state xi(0) (i=1,2,. . .,n), through solvin this group of non-linear differential equations, one can
obtain the moving-trace of the network status.
If the system is stable, then eventually it will convergto a Stable State through:
Asynchronous Work Mode
Synchronous Work Mode
If implement this function by using previous H/W circuit, then the solving process is very fast and the output of this circuit is the result of equations

 

 

Stability
Define the Energy Function, which is same as in DHNN,
only third item is 0 in DHNN, because dη η η η= 0 for binary function f( . )

 

 

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