Description: annie is an ANN, ie, Artificial Neural Network library developed in C++. It can be used to implement various kinds of neural networks like Multi-Layer Perceptron, Radial basis function networks, Hopfield networks etc.-ANN, ie, Artificial Neural Network library developed i n C. It can be used to implement various kinds of n Eural networks like Multi-Layer 102206. Radial basis function networks, Hopfield networks etc. Platform: |
Size: 492335 |
Author:陈伟 |
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Description: annie is an ANN, ie, Artificial Neural Network library developed in C++. It can be used to implement various kinds of neural networks like Multi-Layer Perceptron, Radial basis function networks, Hopfield networks etc.-ANN, ie, Artificial Neural Network library developed i n C. It can be used to implement various kinds of n Eural networks like Multi-Layer 102206. Radial basis function networks, Hopfield networks etc. Platform: |
Size: 492544 |
Author:陈伟 |
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Description: 本程序实做MLP(Multi-layer perceptron)算法，使用者可以自行设定训练数据集与测试数据集，将训练数据集加载，在2、3维下可以显示其分布状态，并分别设定键节值、学习率、迭代次数来训练其类神经网络，最后可观看辨识率与RMSE（Root Mean squared error）来判别训练是否可以停止。-This procedure is to do MLP (Multi-layer perceptron) algorithm, the user can set their own training data set and test data sets, the training data set is loaded, in the 2,3-dimensional display of their distribution, and were set key section of the value of learning rate, number of iterations to train the neural network can watch the final recognition rate and the RMSE (Root Mean squared error) to determine whether the training can stop. Platform: |
Size: 1213440 |
Author:楊易 |
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Description: 自己编的VC＋＋程序，是关于神经网络的多层感知器的-Own the VC program, on the neural network of multi-layer perceptron Platform: |
Size: 39936 |
Author:kitty |
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Description: 一个神经网络计算的库，实现几个通用神经网络体系和训练方法，自识别图，弹性网络等.like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning.-In this article, a C# library for neural network computations is described. The library implements several popular neural network architectures and their training algorithms, like Back Propagation, Kohonen Self-Organizing Map, Elastic Network, Delta Rule Learning, and Perceptron Learning. The usage of the library is demonstrated on several samples:
• Classification (one-layer neural network trained with perceptron learning algorithms)
• Approximation (multi-layer neural network trained with back propagation learning algorithm)
• Time Series Prediction (multi-layer neural network trained with back propagation learning algorithm)
• Color Clusterization (Kohonen Self-Organizing Map)
• Traveling Salesman Problem (Elastic Network). Platform: |
Size: 242688 |
Author:calford |
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Description: 是BP神经网络程序：BP神经网络模型是一个多层感知机构，是由输入层、中间层（隐层）和输出层构成的前馈网络，只含有一个中间层的BP神经网络模型。-BP neural network program：BP neural network model is a multi-layer perceptron institutions, is the input layer, middle layer (hidden layer) and output layer feedforward network, containing only an intermediate layer BP neural network model。 Platform: |
Size: 6144 |
Author:马佳 |
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Description: This paper investigates a new face recognition system based on an efficient design of classifier using SIFT (Scale
Invariant Feature Transform) feature keypoint. This proposed system takes the advantage of SIFT feature which possess strong
robustness to the expression, accessory, pose and illumination
variations. One MLP (Multi Layer Perceptron) based network is
adopted as classifier of SIFT keypoint feature. The proposed
classifier classifies each keypoint into face ID then an ID index histogram counting method is applied as the identification method to recognize face images. Also a bootstrapping method is investigated to select training images during training MLP. The performance of face recognition in some challenging databases is improved efficiently. Platform: |
Size: 122880 |
Author:tongliu |
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Description: 使用matlab不依靠库函数编写的BP网络，多层感知器，以及利用BP网络做的函数逼近。-Use matlab not rely on library functions of BP network to write, multi-layer perceptron, and the use of BP network do function approximation.
Platform: |
Size: 8192 |
Author:刘颖 |
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Description: 基于scala实现的BP多层神经网络源程序，带有测试用例-n implementation in Scala written by Yann Nicolas
This Scala implementation of a neural network, allows the execution and the training of a multi-layer perceptron. Platform: |
Size: 7168 |
Author:hwzhao |
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Description: Major features
BNT supports many types of conditional probability distributions (nodes), and it is easy to add more.
Tabular (multinomial)
Gaussian
Softmax (logistic/ sigmoid)
Multi-layer perceptron (neural network)
Noisy-or
Deterministic
Platform: |
Size: 12275013 |
Author:SunStacy |
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Description: An important aspect of an ANN model is whether it needs
guidance in learning or not. Based on the way they learn, all
artificial neural networks can be divided into two learning
categories - supervised and unsupervised.
• In supervised learning, a desired output result for each input
vector is required when the network is trained. An ANN of the
supervised learning type, such as the multi-layer perceptron, uses
the target result to guide the formation of the neural parameters. It
is thus possible to make the neural network learn the behavior of
the process under study.
• In unsupervised learning, the training of the network is entirely
data-driven and no target results for the input data vectors are
provided. An ANN of the unsupervised learning type, such as the
self-organizing map, can be used for clustering the input data and
find features inherent to the problem.-An important aspect of an ANN model is whether it needs
guidance in learning or not. Based on the way they learn, all
artificial neural networks can be divided into two learning
categories - supervised and unsupervised.
• In supervised learning, a desired output result for each input
vector is required when the network is trained. An ANN of the
supervised learning type, such as the multi-layer perceptron, uses
the target result to guide the formation of the neural parameters. It
is thus possible to make the neural network learn the behavior of
the process under study.
• In unsupervised learning, the training of the network is entirely
data-driven and no target results for the input data vectors are
provided. An ANN of the unsupervised learning type, such as the
self-organizing map, can be used for clustering the input data and
find features inherent to the problem. Platform: |
Size: 125952 |
Author:Vishal |
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Description: 人工神经网络经典教材，系统讲述学习过程、单层感知器、多层感知器、径向基函数网络等到，已绝版-Artificial neural network and classical teaching material, the system about the learning process, single-layer perceptron, multi-layer perceptron, radial basis function network, has been out of print Platform: |
Size: 18356224 |
Author:周大鱼 |
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Description:
深度学习的概念源于人工神经网络的研究。含多隐层的多层感知器就是一种深度学习结构。深度学习通过组合低层特征形成更加抽象的高层表示属性类别或特征，以发现数据的分布式特征表示。[1]
深度学习的概念由Hinton等人于2006年提出。基于深信度网(DBN)提出非监督贪心逐层训练算法，为解决深层结构相关的优化难题带来希望，随后提出多层自动编码器深层结构。此外Lecun等人提出的卷积神经网络是第一个真正多层结构学习算法，它利用空间相对关系减少参数数目以提高训练性能。[1]
深度学习是机器学习研究中的一个新的领域，其动机在于建立、模拟人脑进行分析学习的神经网络，它模仿人脑的机制来解释数据，例如图像，声音和文本。
同机器学习方法一样，深度机器学习方法也有监督学习与无监督学习之分．不同的学习框架下建立的学习模型很是不同．例如，卷积神经网络（Convolutional neural networks，简称CNNs）就是一种深度的监督学习下的机器学习模型，而深度置信网（Deep Belief Nets，简称DBNs）就是一种无监督学习下的机器学习模型。
-Research on the concept of deep learning the artificial neural network. Multi hidden layer of multi-layer perceptron is a deep learning structure. Deep learning features a more abstract representation of a higher level representation of a feature class or feature to find data. [1]
Deep learning s concept by Hinton et al. In 2006. Based on the (DBN), a non supervised greedy layer by layer training algorithm is proposed to solve the problems of the deep structure. In addition, the convolutional neural network proposed by Lecun et al is the first real multi layer structure learning algorithm, which uses the relative relationship between the number of parameters to improve the training performance. [1]
Deep learning is a new field in machine learning research. The motivation is to establish and simulate the human brain to analyze the learning of neural network, which simulates the human brain mechanism to explain the data, such as image, sound and text.
Convolutional (neural) net Platform: |
Size: 67584 |
Author:Francis |
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Description: 感知神经网络学习，多层感知器完成异或功能实现代码，供交流学习使用-Perceptual learning of neural network, multi-layer perceptron perform the XOR function implementation code used by the exchange of learning Platform: |
Size: 484352 |
Author:zz |
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Description: method based on Neural
Network (NN) technique and accompanied with MMSE
(Minimum Mean Square Error), which corrects at the receiver
level, the Non-Linear (NL) distortions due to the HPA (High
Power Amplifier).-In this paper, we present a method based on Neural
Network (NN) technique and accompanied with MMSE
(Minimum Mean Square Error), which corrects at the receiver
level, the Non-Linear (NL) distortions due to the HPA (High
Power Amplifier). The neural network consists on a feedforward
Multi-Layer Perceptron (MLP) associated with
Levenberg-Marquardt learning algorithm. The results show
that the neural network compensator brings perceptible in a
complete VBLAST MIMO OFDM (Vertical Bell Laboratories
Layered Space-Time Multiple-Input Multiple-Output
Orthogonal Frequency Division Multiplexing) system running
under a Rayleigh fading channel. Platform: |
Size: 330752 |
Author:wangxx |
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Description: （神经网络）多个隐含层的多层感知器网络训练数据得到网络，并使用测试数据统计所设计多层感知器的平均识别正确率-Multi layer perceptron network training data with multiple hidden layers is obtained, and the average recognition accuracy of the multi-layer perceptron is designed by using the test data statistics. Platform: |
Size: 22528 |
Author:张三 |
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Description: 生物地理算法优化多层感知网络进行特征分类和识别，精度高达97 -Bio geographic algorithm to optimize multi layer perceptron network for feature classification and recognition, the accuracy of up to 97 Platform: |
Size: 232448 |
Author:antry |
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Search - Multi layer perceptron network