Description: 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 Platform: |
Size: 63147 |
Author:马行田 |
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Description: 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 Platform: |
Size: 62464 |
Author:马行田 |
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Description: 人工只能的重要分之之一就是神经网络，而神经网络当中的前馈网络BP算法网络应用最为广泛-Artificial only one of the important parts per neural network, while the neural network feedforward network which BP algorithm most widely used network Platform: |
Size: 1024 |
Author: |
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Description: 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 Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006.
-I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew, Platform: |
Size: 2048 |
Author:chenlei |
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Description: 摘要：给出了解决机器人控制问题一种神经网络方法。使用一个分级神经网络(NN)结构学习刚体机器人动力学特点。对于一般类别的机械手，使用前训练一系列的三层前馈网络模块，然后把这些基函数实时地用于第四层。使用线性控制原理，辅以非线性补偿控制机械手，使学得的机械手动力学知识创建一个在整个工程中高速控制机械手的控制器。模拟结果表明控制器的性能得到了大大提高。-Abstract: This paper presents a solution to the issue of robot control neural network method. Using a hierarchical neural network (NN) structure to study the characteristics of rigid robot dynamics. For the general category of the mechanical hand, the use of a series of three training feedforward network module, and then these basis functions in real time for the fourth floor. The use of linear control theory, supplemented by non-linear compensation control robot to learn the robot dynamics knowledge to create a project in the entire high-speed control manipulator controller. Simulation results show that the controller performance has been greatly enhanced. Platform: |
Size: 40960 |
Author:范卫华 |
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Description: 过程信号的前馈-反馈型自适应盲分离算法：利用神经网络的自学习能力实现信号的盲分离已被证明是实现信号分离的一种有效方法,不同的神经网络模型对分离算法的效能将产生极大的影响
-The process of signal feedforward- feedback-based adaptive algorithm for blind source separation: Using neural networks to achieve self-learning ability of the blind signal separation has been proved to achieve the signal separation is an effective method, different neural network model of the separation algorithm efficiency will be have a great impact on the Platform: |
Size: 57344 |
Author:李玉 |
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Description: 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 [1995], such as the continuous development and improvement has been successfully applied to machine learning, artificial intelligence, automatic control and many other fields. Probabilistic neural network than the multi-layer feedforward network of simple math, and are easy to achieve Platform: |
Size: 8192 |
Author:piano |
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Description: 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 Bayesian Classifier, the basic concepts of association rules, data mining algorithms, decision tree method in data mining applications, training Bayesian belief networks, to the spread of Bayesian belief networks, to the dissemination and interpretability, multi-layer feedforward neural network Platform: |
Size: 184320 |
Author:fast man |
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Description: 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. Platform: |
Size: 1024 |
Author:肖晔 |
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Description: Neural Network Based Clustering
using Self Organizing Map (SOM) in Excel
Here is a small tool in Excel using which you can find clusters in your data set. The tool uses Self Organizing Maps (SOM) - originally proposed by T.Kohonen as the method for clustering.
* Neural Network based Clustering tool in Excel (209 KB in Zipped format. 947 KB when unzipped.)
Inside the downloaded zip file, you will find the Excel file containing the application. Before running it, I suggest that you go through the ReadMe worksheet. It contains brief instructions on how to run the tool.
If you are interested in building Prediction and Classification models in Excel using Feedforward-Backpropagation Neural Network, here are two small Excel based tools for you. Also, if you are interested in Tree based Classification models, here is a Tree based classifier in Excel.
-Neural Network Based Clustering
using Self Organizing Map (SOM) in Excel
Here is a small tool in Excel using which you can find clusters in your data set. The tool uses Self Organizing Maps (SOM)- originally proposed by T.Kohonen as the method for clustering.
* Neural Network based Clustering tool in Excel (209 KB in Zipped format. 947 KB when unzipped.)
Inside the downloaded zip file, you will find the Excel file containing the application. Before running it, I suggest that you go through the ReadMe worksheet. It contains brief instructions on how to run the tool.
If you are interested in building Prediction and Classification models in Excel using Feedforward-Backpropagation Neural Network, here are two small Excel based tools for you. Also, if you are interested in Tree based Classification models, here is a Tree based classifier in Excel.
Platform: |
Size: 214016 |
Author:Jessie |
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Description: design and implementation of feedforward neural network with BP training algorithm.(include the GUI) Platform: |
Size: 564224 |
Author:maisam |
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Description: 本文提出一种粗糙集理论和动态前馈神经网络相结合的神经网络构造方法。充分发挥了粗糙集理论和神经网络的优势，弥补了各自的缺点。并应用于实际工业过程，在乙烯装置裂解炉燃料气热值控制中取得了良好的应用效果。-This paper presents a rough set theory and dynamic feedforward neural networks combined neural network constructed. Give full play to the rough set theory and neural networks the advantage to make up for their shortcomings. And applied to practical industrial processes, in the ethylene plant cracking furnace fuel gas calorific value of control to achieve a good application effect. Platform: |
Size: 402432 |
Author:王飞 |
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Description: 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 algorithm, multilayer feedforward network and the BP learning algorithm, support to the recording machine and its learning algorithm, Hopfield neural network, stochastic neural networks and simulated annealing algorithm, actually God of war and synergistic neural network fiber network. Given in each chapter of the simulation based on MATLAB and practice. Platform: |
Size: 6059008 |
Author:小龙 |
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Description: 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)。-BP (Back Propagation) network in 1986 by Rumelhart and McCelland led team of scientists proposed an algorithm by error back propagation trained multilayer feedforward network, is currently the most widely used one neural network model. BP network can learn and store a lot of input- output model mapping, without prior mapping reveals the mathematical description of this equation. Its learning rule is to use the steepest descent method, by back-propagation network to continuously adjust the weights and thresholds, so the network and the minimum sum of squared errors. BP neural network topology, including the input layer (input), hidden layer (hide layer) and output layer (output layer). Platform: |
Size: 2048 |
Author:陈财雄 |
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Description: 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)。-BP (Back Propagation) network in 1986 by Rumelhart and McCelland led team of scientists proposed an algorithm by error back propagation trained multilayer feedforward network, is currently the most widely used one neural network model. BP network can learn and store a lot of input- output model mapping, without prior mapping reveals the mathematical description of this equation. Its learning rule is to use the steepest descent method, by back-propagation network to continuously adjust the weights and thresholds, so the network and the minimum sum of squared errors. BP neural network topology, including the input layer (input), hidden layer (hide layer) and output layer (output layer). Platform: |
Size: 1024 |
Author:陈财雄 |
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Description: 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)。-BP (Back Propagation) network in 1986 by Rumelhart and McCelland led team of scientists proposed an algorithm by error back propagation trained multilayer feedforward network, is currently the most widely used one neural network model. BP network can learn and store a lot of input- output model mapping, without prior mapping reveals the mathematical description of this equation. Its learning rule is to use the steepest descent method, by back-propagation network to continuously adjust the weights and thresholds, so the network and the minimum sum of squared errors. BP neural network topology, including the input layer (input), hidden layer (hide layer) and output layer (output layer). Platform: |
Size: 2048 |
Author:陈财雄 |
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Description: 用一个多层前向神经网络，采用反向传播算法依据控制要求实时输出Kp、Ki、Kd，依次作为PID控制器的实时参数，代替传统PID参数靠经验的人工整定和工程整定，以达到对大迟延主气温系统的良好控制。-We use a multilayer feedforward neural network using back propagation algorithm and based on control requirements.This net can real-time output Kp, Ki, Kd as the PID controller parameters ,insteading of the traditional PID parameters determined by experience. So it can obtain good control performance . Platform: |
Size: 577536 |
Author:durongmao |
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Description: 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)。-BP (Back Propagation) network is proposed in 1986, a team of scientists led by Rumelhart and McCelland, is one kind according to the error back-propagation algorithm for training multilayer feedforward network, is one of the most widely used at present neural network model. BP network can learn and store a lot of input- output model mapping, without prior to reveal the mathematical equations describing the mapping relation. Its learning rule is to use the steepest descent method, through the back-propagation network to continuously adjust the weights and thresholds of the network, the error square and minimum. BP neural network topology, including input layer, hidden layer (input) (hide layer) and the output layer (output layer). Platform: |
Size: 4096 |
Author:陈发东 |
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Search - feedforward network