Gindi G R, Gmitro A F, Parthasarathy K
Appl Opt. 1988 Jan 1;27(1):129-34. doi: 10.1364/AO.27.000129.
The discrete-valued neural network proposed by Hopfield requires zero-diagonal terms in the memory matrix so that the net evolves toward a local minimum of an energy function. For a version of this model with bipolar nodes and positive terms along the diagonal, the net evolves so that only updates that lower the energy by a sufficient amount are accepted. For a net programmed as an outer-product associative content-addressable memory, the version with nonzero-diagonal elements performs nearly identically to one with zero-diagonal terms, and the dropping of the zero-diagonal requirement is advantageous for optical implementation.
霍普菲尔德提出的离散值神经网络要求记忆矩阵中的对角项为零,以便网络朝着能量函数的局部最小值演化。对于具有双极节点且对角线上为正项的该模型版本,网络会这样演化,即只有那些能使能量充分降低的更新才会被接受。对于被编程为外积联想式内容可寻址存储器的网络,具有非零对角元素的版本与具有零对角项的版本表现几乎相同,并且去掉零对角要求对于光学实现是有利的。