Description: 请选用多层前馈网络和一种改进的BP算法对下面的系统进行辨识仿真，给出辨识的仿真结果 -Please choose multi-layer feedforward networks and an improved BP algorithm to identify the following system simulation, the simulation results are given recognition Platform: |
Size: 3072 |
Author:桦华 |
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Description: 请选用多层前馈网络和一种改进的BP算法对下面的多输入系统进行辨识仿真 -Please choose multi-layer feedforward networks and an improved BP algorithm to the following multi-input system for identification of simulation Platform: |
Size: 4096 |
Author:桦华 |
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Description: 利用神经网络实现模式识别的程序,第三章是预处理程序,第四章是有导前馈网络,第九章是hopfield网络,第十章是ART原理程序
-Realize the use of neural network pattern recognition procedure, the third chapter is the pre-processing procedures, in Chapter IV is mediated feedforward networks, is hopfield IX network, Chapter X is the principle of ART procedures Platform: |
Size: 150528 |
Author:chenyuwen |
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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: * 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 want to use
* needlessly complex neural network libraries. It features multilayer
* feedforward perceptron neural networks, sigmoidal activation function
* with bias, backpropagation training with settable learning rate and
* momentum, and backpropagation training in batches.
-* 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 want to use* needlessly complex neural network libraries. It features multilayer* feedforward perceptron neural networks, sigmoidal activation function* with bias, backpropagation training with settable learning rate and* momentum, and backpropagation training in batches. Platform: |
Size: 29696 |
Author:正熹 |
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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: 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: 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 Platform: |
Size: 1024 |
Author:周凯 |
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Description: 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:Gauss变异动态调整BP算法中学习率参数和冲量系数-This program is a three-layer error back propagation feedforward neural networks supervised learning algorithm: the Gauss variation dynamically adjusts the learning rate in BP algorithm parameters and impulse coefficient Platform: |
Size: 292864 |
Author:wanglu |
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Description: 一种快速而准确的线顺序前馈神经网络学习算法,是单隐层的、带径向基函数的网络，比传统神经网络快，且精度高。-an online sequential
learning algorithm for single hidden layer feedforward networks
(SLFNs) with additive or radial basis function (RBF) hidden
nodes in a unified framework Platform: |
Size: 6144 |
Author:wxd |
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Description: 基于多层前向网络的诊断模型在设备 故障诊断领域应用比较广泛。 但在多层前向网络的
设计和训练问题上, 单隐层的隐层单元数选取一直非常困难, 一般采用试凑 法, 既费时又 不一定保证收敛。-Diagnostic model based on multilayer feedforward networks in the field of equipment fault diagnosis used widely. But before multilayer design and training issues on the network, the number of hidden layer unit single hidden layer selection has been very difficult, generally use trial and error method, time-consuming and does not necessarily guarantee convergence. Platform: |
Size: 193536 |
Author:张力 |
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Description: 为了评价ELM的性能，试分别将ELM应用于基于近红外光谱的汽油辛烷值测定和鸢尾花种类识别两个问题中,并将其结果与传统前馈网络(BP、RBF、PNN、GRNN等)的性能和运行速度进行比较,并探讨隐含层神经元个数对ELM性能的影响。(n order to evaluate the performance of ELM, test the application of ELM in the near infrared spectroscopy based on the determination of gasoline octane value and two kinds of problems of iris recognition, and the results of the traditional feedforward networks (BP, RBF, PNN, GRNN) to compare the performance and speed, and to explore the hidden layer number effect on the performance of ELM neurons.) Platform: |
Size: 177152 |
Author:panyuhang
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Description: 概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出，是径向基网络的一个分支，属于前馈网络的一种。它具有如下优点：学习过程简单、训练速度快；分类更准确，容错性好等。从本质上说，它属于一种有监督的网络分类器，基于贝叶斯最小风险准则。(The rate neural network, first proposed in 1989, is a branch of the RBF network and is one of the feedforward networks. It has the following advantages: the learning process is simple, the training speed is fast, the classification is more accurate, the fault tolerance is good, and so on. In essence, it belongs to a supervised network classifier based on Bayesian minimum risk criteria.) Platform: |
Size: 46080 |
Author:哼哼1214
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Description: deep learning 书籍，此书包括机器学习基础，深度前馈网络，卷积网络，蒙特卡洛方法等的详细介绍(Deep learning books, which include a detailed introduction to machine learning, deep feedforward networks, convolution networks, Monte Carlo methods, and so on) Platform: |
Size: 18977792 |
Author:过路的妖怪 |
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Search - feedforward networks