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NeuralNetworks
- Neural Networks at your Fingertips.rar =============== Network: Adaline Network =============== Application: Pattern Recognition Classification of Digits 0-9 Author: Karsten Kutza Date: 15.4.96 Reference: B. Widrow, M
LMS
- 这是一个来自权威书籍的最小均方算法(LMS algorithm),也叫Widrow-Hoff算法。-This is a book from the authority of the least mean square algorithm (LMS algorithm), also known as Widrow-Hoff algorithm.
lecture03-090302
- 神经网络教程—— Performance Surfaces —— Performance Optimization —— Widrow-Hoff Learning-Neural Network Tutorial- Performance Surfaces - Performance Optimization - Widrow-Hoff Learning
adaptive_filter
- 最小均方自适应滤波器（LMS）是由Widrow和Hoff在1960年始创，它是以横向（即抽头延时线）结构为基础构建的。其最基本的形式设计简单，性能也高度高效。这两个特性使得LMS滤波器在各种应用中非常流行。递归最小二乘自适应滤波器（RLS）由于可提供快收敛速率而且对输入信号相关矩阵特征值扩散度变化不敏感，从而突破了LMS族的某些实际限制；其代价是增加了计算的复杂度。-the LMS algorithm of adaptive filter
lms
- LMS由美国斯坦福大学的Widrow和Hoff在研究自适应理论时提出的，由于其容易实现而很快得到了广泛应用，成为自适应滤波的标准算法。 -LMS Widrow of Stanford University by the United States and Hoff adaptive theory put forward in the study, because of its easy to implement and will soon be widely used as a stand
LMS_P101_1
- 改程序为《自适应信号处理》（Bernard Widrow著，王永德等译）书中101页上图牛顿法的MATLAB程序，仿真效果和书上一致 -Change the program to " Adaptive Signal Processing" (Bernard Widrow a, s Acoustic other translation) book 101 on the map Newton MATLAB programs, simulation results and the
widrow_hoff
- a simple widrow hoff implementatiion
Widrow-Hoff-Training-Method
- The WH class is an implementation of the Widrow-Hoff. The Widrow-Hoff (WH) algorithm, often called Least Mean Square (LMS), is an online-algorithm. The WH can be interpreted as a gradient descent procedure on the error space. In other words, the
arna_wh
- The training of a ADALINE neuron with one entrance using the Widrow - Hoff algorithm
WidrowHoff
- Widrow-Hoff(LMS) : Linear Discriminants
LMS
- 由美国斯坦福大学的Widrow和Hoff在研究自适应理论时提出的LMS算法，由于其容易实现而很快得到了广泛应用，成为自适应滤波的标准算法。-LMS algorithm has been widely used due to its easy to implement, become=ing the standard of the adaptive filtering algorithm.
LMS
- 感知器和自适应线性元件在历史上几乎是同时提出的，并且两者在对权值的调整的算法非常相似。它们都是基于纠错学习规则的学习算法。感知器算法存在如下问题：不能推广到一般的前向网络中；函数不是线性可分时，得不出任何结果。而由美国斯坦福大学的Widrow和Hoff在研究自适应理论时提出的LMS算法，由于其容易实现而很快得到了广泛应用，成为自适应滤波的标准算法。-Least mean square error algorithm
131848141
- 模式识别线性判别算法，包括感知器算法，最小方差准则分类算法和Widrow-Hoff算法-Pattern recognition linear discriminant algorithms, including perception algorithm, minimum variance criterion classification algorithm and Widrow-Hoff algorithm
Widrow-Hoff-Learning-Algorithm
- 编写了简单的Widrow-Hoff学习算法，绘制学习速率在0.01、0.1和0.5时的误差变化趋势，迭代了5000次，另外一个描绘了三种情况的分解面-It is about the Widrow-Hoff learning algorithm.The algorithm sets three different learnging rate as 0.01,0.1 and 0.5.
anymin
- 本程序使用GMDH网络对交通的流量进行预测，输入的数据为连续n天的m组流量数据。 输出数据为第n+1天的m组的流量的预测数据。 每个神经元的学习方式为widrow-hoff 在学习过程中，每一层否挑选15个优秀的神经元保留到下一层(This procedure uses GMDH network traffic flow prediction, the input data for the continuous n days of M group traffic data. The ou