CAJ | 학술논문

针对Hopfield NN传统设计方法要求权值矩阵需要满足对称的约束，以及记忆容量和容错性低，且记忆模式易陷入伪稳定状态的缺点，本文提出了利用遗传算法（ GA）优化设计Hopfield NN权值的方法，并与传统方法对比，验证了GA?Hopfield NN具有较大的记忆容量和较强的容错性。同时提出了GA?Hopfield NN的板形模式识别模型设计方案，将具有较强计算能力的反馈网络用于实时信息处理系统实现模式识别，克服了当前板形智能识别模型动态性差，容错能力低及实时性差的缺陷。同时，Hopfield NN的二值计算形式大大提高了系统的运算速度，为硬件实现和工程应用提供了新思路。
침대Hopfield NN전통설계방법요구권치구진수요만족대칭적약속，이급기억용량화용착성저，차기억모식역함입위은정상태적결점，본문제출료이용유전산법（ GA）우화설계Hopfield NN권치적방법，병여전통방법대비，험증료GA?Hopfield NN구유교대적기억용량화교강적용착성。동시제출료GA?Hopfield NN적판형모식식별모형설계방안，장구유교강계산능력적반궤망락용우실시신식처리계통실현모식식별，극복료당전판형지능식별모형동태성차，용착능력저급실시성차적결함。동시，Hopfield NN적이치계산형식대대제고료계통적운산속도，위경건실현화공정응용제공료신사로。
A genetic algorithm GA to optimize weights of Hopfield NN called GA?Hopfield NN structure is proposed in the light of disadvantages of the traditional design method for Hopfield NN such as low memory capacity and error tolerant memory models easily falling into the pseudo steady state and weight matrix requested to be symmetry.GA?Hopfield NN has a larger memory capaci?ty and a stronger error tolerant than that of traditional methods.A new flatness pattern recognition model based on GA?Hopfield NN is also set up.Feedback network that has strong computing ability is applied into real time information handling system to realize pat?tern recognition.Many defects that exists in current flatness intelligent recognition model such as poor dynamic low error tolerant and bad real?time are conquered.Meanwhile Hopfield NN adopts binary calculation form improves the operation speed of the sys?tem greatly and provides a new way of the hardware realization and engineering application.