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Description: 模式识别课程作业-神经网络分类IRIS数据集.共两层网络，程序有详细注释。程序结果将输出到EXCEL文件中，也很详细。-Course work in pattern recognition- Neural Network Classification IRIS data set. A total of two networks, a detailed program notes. Program results will be output to the EXCEL file, and very detailed.
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Size: 2048 |
Author: yumingwei |
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Description: backpropagation using java
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Size: 58368 |
Author: theknight47 |
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Description: backpropagation in java
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Size: 382976 |
Author: tomi |
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Description: backpropagation code in matlab
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Size: 2048 |
Author: maryam naghdiani |
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Description: This file include implementation of backpropagation algorithm in java
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Size: 2048 |
Author: qwueene |
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Description: The Adaline is essentially a single-layer backpropagation network. It is trained on a pattern recognition task, where the aim is to classify a bitmap representation of the digits 0-9 into the corresponding classes. Due to the limited capabilities of the Adaline, the network only recognizes the exact training patterns. When the application is ported into the multi-layer backpropagation network, a remarkable degree of fault-tolerance can be achieved.
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Size: 3072 |
Author: ali |
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Description: Backpropagation Implementation with Java
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Size: 3072 |
Author: syahid05 |
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Description: 307 - Backpropagation Neural Network v1.0 - Namira
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Size: 58368 |
Author: Nima |
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Description: With 2 Hidden Layer Backpropagation Using Matlab
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Size: 2048 |
Author: Aldy |
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Description: DETAILS OF BACKPROPAGATION
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Size: 769024 |
Author: tar |
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Description: backpropagation alogorithmnto implement the neural netwrk computation for variable features n hidden layers
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Size: 51200 |
Author: aaditya |
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Description: this to solve the problem of xnor using backpropagation algorithm-this is to solve the problem of xnor using backpropagation algorithm
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Size: 1024 |
Author: Aead Amer |
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Description: this simple implementation of forecasting using neural network backpropagation-this is simple implementation of forecasting using neural network backpropagation
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Size: 2048 |
Author: kadal |
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Description: Backpropagation, an abbreviation for backward propagation of errors , is a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of a loss function with respects to all the weights in the network.-Backpropagation, an abbreviation for backward propagation of errors , is a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of a loss function with respects to all the weights in the network.
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Size: 4096 |
Author: ati |
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Description: backpropagation using matlabs
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Size: 1024 |
Author: niecha |
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Description: 简单的BP神经网络小程序，识别4*4像素大小的A,I,O字符-This is a simple program of backpropagation recognizing the labels of A,I and O.
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Size: 36864 |
Author: santongwei |
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Description: for backpropagation learning
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Size: 1024 |
Author: suci |
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Description: The following java code is based on a multi-layer
Back Propagation Neural Network Class (BackPropagation.class)
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Size: 3072 |
Author: tt77 |
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Description: The main contribution of this paper is using
optimal control theory for improving the convergence
rate of backpropagation algorithm. In the proposed
approach, the learning algorithm of backpropagation
is modeled as a minimum time control problem
in which the step-size of its learning factor is considered
as the input of this model. In contrast to the traditional
backpropagation, learning algorithms which
the step-size by trial and error, it is selected
adaptively based on optimal control criterion. The effectiveness
of the proposed algorithm is uated in
two simulations: XOR and 3-bit parity. In both simulation
examples, the proposed algorithm outperforms
well in speed and the ability to escape local minima.-The main contribution of this paper is using
optimal control theory for improving the convergence
rate of backpropagation algorithm. In the proposed
approach, the learning algorithm of backpropagation
is modeled as a minimum time control problem
in which the step-size of its learning factor is considered
as the input of this model. In contrast to the traditional
backpropagation, learning algorithms which
the step-size by trial and error, it is selected
adaptively based on optimal control criterion. The effectiveness
of the proposed algorithm is uated in
two simulations: XOR and 3-bit parity. In both simulation
examples, the proposed algorithm outperforms
well in speed and the ability to escape local minima.
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Size: 415744 |
Author: samir |
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Description: backpropagation algorithm for train nn
but it have some problem. its need to redo
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Size: 43008 |
Author: emin |
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