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Single-layer neural networks can be trained using various learning algorithms. The best-known algorithms are the Adaline, Perceptron and Backpropagation algorithms for supervised learning. The first two are specific to single-layer neural networks wh
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* Lightweight backpropagation neural network.
* This a lightweight library implementating a neural network for use
* in C and C++ programs. It is intended for use in applications that
* just happen to need a simply neural network and do not
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利用随机反向传播网络归类的学习算法。它是LMS算法的自然延伸，也是多层神经网络的有监督训练。-Classify using a backpropagation network with stochastic learning algorithm。
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Neural network source code with 2 learning process using backpropagation algorithms.
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DigitalEyes is an OCR (Optical Character Recognizer) developed in C/Caml.
usage : ./digitaleyes[.opt] [processes in chronological order] file
-noise Reduces image noise.
-rotate Finds angle and rotate image.
-lessons Builds the lessons
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Classify using a backpropagation recurrent network with a batch learning algorithm
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fortran的神经网络训练程序。
Phil Brierley提供
更多信息见 www.philbrierley.com
-Neural Network in Fortran90
!! Multilayer Perceptron trained with
!! the backpropagation learning algorithm
!! coded in Fortran90 by Phil Brierley
!! www.philbrierley.com
!
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This book provides illustrative examples in C++ that the reader can use as a basis for further experimentation. A key to learning about neural networks to appreciate their inner workings is to experiment. Neural networks, in the end, are fun to learn
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Backpropagation with momentum *
by Andres Perez-Uribe *
References :
- G. Hinton, "How neural networks learn from experience",
Scientific American, sep 1992.
- P. Werbos, "The Roots of Backpropagation: From ordered derivatives
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单输出函数Y=SIN(X)逼近问题的bp程序:假设网络结构为3--2--1，输入维数M,共N个样本,一般输入不算层，输出算层- 激活函数: hardlim---(0,1),hardlims---（-1,1),purelin,logsig---(0,1),tansig----(-1,1)
softmax,poslin,radbas,satlin,satlins,tribas
训练算法: 1.traingd,traingdm,traingda(variable l
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MATLAB neural network backprop code
This code implements the basic backpropagation of
error learning algorithm. The network has tanh hidden
neurons and a linear output neuron.
adjust the learning rate with the slider
-MATL
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深度学习（Deep learning）matlab工具包-`NN/` - A library for Feedforward Backpropagation Neural Networks
`CNN/` - A library for Convolutional Neural Networks
`DBN/` - A library for Deep Belief Networks
`SAE/` - A library for Stacked Auto-
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这是一个四个不同的S函数实现集合的递归模糊神经网络（RFNN）。该网络采用了4组可调参数,这使得它非常适合在线学习/操作，从而可应用到系统识别等方面。-This is a collection of four different S-function implementations of the recurrent fuzzy neural network (RFNN) described in detail in [1]. It is a four-layer, neuro-fuzzy net
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for backpropagation learning
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这是用于深度学习的Matlab工具箱
深度学习是机器学习的一个新的子领域，专注于学习深层次的数据模型。
它的灵感来自于人类大脑的明显的深层次（分层的）层次结构。
目录包括`NN /` - 一个用于前馈反向传播神经网络的库，`CNN /` - 卷积神经网络库，`SAE /` - 堆叠式自动编码器库，`CAE /` - 卷积自动编码器库，`util /` - 库使用的功能函数，`data /` - 实例使用的数据，`tests /` - 单元测试来验证工具箱是否正常工作(A Matlab to
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深度学习允许由多个处理层组成的计算模型来学习具有多个抽象层次的数据表示。这些方法极大地提高了语音识别、视觉对象识别、目标检测以及药物发现和基因组学等许多领域的最新进展。深度学习发现复杂的结构在大数据集，通过使用反向传播算法来指示一台机器应该如何改变其内部参数，用于计算在每一层的代表性，从上一层的代表。深层卷积网在处理图像、视频、语音和音频方面取得了突破性进展，而递归网络则在文本和语音等连续数据上起到了作用。(Deep learning allows computational models th
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机器学习中神经网络的后向传播算法的相关推导(The derivation of backpropagation algorithm for neural networks in machine learning)
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深度学习代码CNN，用VC++ 实现，很有参考价值，神经网络权值参数反向传播实现。(Deep learning code CNN, implemented with VC++, is of great reference value, and neural network weight parameter backpropagation is realized.)
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Biological systems are able to recognise temporal sequences of stimuli or compute
in the temporal domain. In this paper we are exploring whether a biophysical
model of a pyramidal neuron can detect and learn systematic time
delays between the spik
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