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Neural_Network_Code_CHAPT9
- LAM（线性联想记忆）算法：单层的前馈网络，例子能够实现自联想功能-LAM (Linear associative memory) algorithm : the first single-layer feedforward network, for example, can be realized since Lenovo function
IncrementalRandomNeurons
- 本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hid
Fortran_bp
- BP（Back Propagation）网络是1986年由Rumelhart和McCelland为首的科学家小组提出，是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。-
PNN
- PNN又称为概率神经网络,它最初由数学家Specht于1990年提出,后经Master[1995]等不断发展和完善,已成功地应用于机器学习、人工智能、自动控制等众多领域.概率神经网络比多层前馈网络的数学原理简单,且易于实现-PNN is also known as the probabilistic neural network, which was first introduced by the mathematician Specht in 1990, after the Master [1
NNCONTROLINGANDBP
- 神经网络控制作为一种全新的智能控制方法,是解决非线性系统预测和控制问题的一种重要手段,受到了许多专家学者的广泛关注,是目前国内外研究的热点之一。本文着重研究前向神经网络的学习算法,简单探讨了BP算法在逼近非线性系统中各个因素对系统的影响。 -Neural Network Control as a new intelligent control method is to solve the nonlinear system to predict and control an important
33
- BP算法是为了解决多层前向神经网络的权系数优化而提出来的；所以，BP算法也通常暗示着神经网络的拓扑结构是一种无反馈的多层前向网络-BP algorithm is to solve the multi-layer feedforward neural network weights optimization and put forward Therefore, BP algorithm usually implies that the topology of neural network is
bp_back
- 本程序为一个误差向后传播的三层前馈神经网络有指导的学习算法。-This procedure for a transmission error backward three feedforward neural network learning algorithm for guidance.
lightweightbackpropagationneuralnetwork
- * 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
BP
- 反向传播算法也称BP算法，是一种神经网络学习的数学模型，解决多层前向神经网络的权系数优化-Back-propagation algorithm, also known as BP algorithm is a neural network study of the mathematical model and solve multi-layer feedforward neural network weights optimization
data-dig
- 一些数据挖掘算法相关,包含定义网络拓扑,有关高血压研究方面的数据,朴素贝叶斯分类,关联规则基本概念,数据挖掘算法, 决策树方法在数据挖掘中的应用,训练贝叶斯信念网络,后向传播,贝叶斯信念网络,后向传播和可解释性,多层前馈神经网络-Some relevant data mining algorithms, including the definition of network topology, the high blood pressure research data, Naive Baye
cmac
- 采用CMAC前馈网络，将CMAC和PID结合起来对系统进行前馈控制-CMAC using feedforward networks, will combine the CMAC and PID of the feed-forward control system
BP
- 我们最常用的神经网络就是BP网络，也叫多层前馈网络。BP是back propagation的所写，是反向传播的意思。-We are the most commonly used neural network is a BP network, also known as multi-layer feedforward network. BP is written by the back propagation is the meaning of back-propagation.
threeneuralnetword
- 如何构造神经网络，及构造一个三层前馈神经网络，来逼近非线性函数-How to construct a neural network, and construct a three-layer feedforward neural network to approximate nonlinear functions
test
- 3层前馈神经网络,java编写。format用于约束输入数据的格式-3-layer feedforward neural network, java write. format the format of the input data for the binding
Neural_Control
- 首先介绍人工神经网络的基本概念和ANN 的特性，以及神经网络的学习方法。然后讲授典型的前向神经网络、反馈神经网络的原理、结构、基本算法，给出了BP 网络的算法改进。最后介绍了神经网络PID 控制。-First introduces the basic concepts of artificial neural networks and the characteristics of ANN, and the neural network learning. And then teach the t
Artificialneuralnetworkandsimulation
- 内容包括:人工神经网络简介、单层前向网络及LMS学习算法、多层前向网络及BP学习算法、支持向录机及其学习算法、Hopfield 神经网络,随机神经网络及模拟退火算法、竟争神经网络和协同神纤网络。每章均给出了基于MATLAB的仿真实例以及练习。 -Contents include: Introduction to artificial neural networks, single-layer feedforward network and the LMS learning algorit
trainigfeedforwardwithgenetic
- this is an e-book about Training Feedforward Neural Networks Using Genetic Algorithms
backprop
- MBP 变速率BP前向网络算法,较为有效地一种训练算法,特点是学习速率的自适应变化。-A modified BP algorithm for feedforward neural network .Unlike usual BP ,it uses a modified learning rate adaption strategy which significanly improves the learning procedure.
mlp-new.cpp
- 多层前馈神经网络学习算法训练与测试才c＋＋代码-Multilayer feedforward neural network learning algorithm training and testing before c++ code
C_bp
- BP（Back Propagation）网络是1986年由Rumelhart和McCelland为首的科学家小组提出，是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。-