计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2009年
31期
22-24
,共3页
自组织神经网络%卡尔曼滤波器%无先导变换
自組織神經網絡%卡爾曼濾波器%無先導變換
자조직신경망락%잡이만려파기%무선도변환
self-organizing feature map%Kalman filter%unscented transform
自组织神经网络的主要目的是将任意维教的输入信号模式转变成为一维或二维的离散映射,并且以拓扑有序的方式自适应地实现这个过程.学习过程中,对邻域宽度函数和学习率函数参数是根据经验选择的,没有一定的规则或方法,因此,邻域保持映射的获得往往先于参数的学习过程.将线性Kalman滤波器和基于无先导变换的Kalman滤波器分别用于学习率函数和邻域宽度函数的预测,可以提高自组织神经网络的学习能力.改进后的算法可以根据输入数据自适应地调整邻域宽度函数和学习率函数.
自組織神經網絡的主要目的是將任意維教的輸入信號模式轉變成為一維或二維的離散映射,併且以拓撲有序的方式自適應地實現這箇過程.學習過程中,對鄰域寬度函數和學習率函數參數是根據經驗選擇的,沒有一定的規則或方法,因此,鄰域保持映射的穫得往往先于參數的學習過程.將線性Kalman濾波器和基于無先導變換的Kalman濾波器分彆用于學習率函數和鄰域寬度函數的預測,可以提高自組織神經網絡的學習能力.改進後的算法可以根據輸入數據自適應地調整鄰域寬度函數和學習率函數.
자조직신경망락적주요목적시장임의유교적수입신호모식전변성위일유혹이유적리산영사,병차이탁복유서적방식자괄응지실현저개과정.학습과정중,대린역관도함수화학습솔함수삼수시근거경험선택적,몰유일정적규칙혹방법,인차,린역보지영사적획득왕왕선우삼수적학습과정.장선성Kalman려파기화기우무선도변환적Kalman려파기분별용우학습솔함수화린역관도함수적예측,가이제고자조직신경망락적학습능력.개진후적산법가이근거수입수거자괄응지조정린역관도함수화학습솔함수.
The principal goal of the self-organizing feature map is to transform an incoming signal pattern of arbitrary dimension into a one- or two-dimensional discrete map,and to perform this transformation adaptively in a topologically ordered fashion.The learning process is controlled by learning coefficient and the width of neighborhood function,which have to be chosen empirically because there aren't exist rules or a method for their calculation.To improve the learning ability of the self-organizing maps, a method is presented,which the learning coefficient and the width of neighborhood function is predicted by linear Kalman fitler and the Kalman filter based on the unscented transform respectively.
