Description: 基于C开发的三个隐层神经网络，输出权值、阈值文件，训练样本文件，提供如下函数：1）初始化权、阈值子程序；2）第m个学习样本输入子程序；3）第m个样本教师信号子程序；4）隐层各单元输入、输出值子程序；5）输出层各单元输入、输出值子程序；6）输出层至隐层的一般化误差子程序；7）隐层至输入层的一般化误差子程序；8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序；9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序；10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序；11）第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序；12）N个样本的全局误差计算子程序。-C development based on the three hidden layer neural network, the output weights, threshold documents, training sample documents, for the following functions : a) initialization, the threshold subroutine; 2) m learning samples imported subroutine; 3) m samples teachers signal Subroutine ; 4) hidden layer of the module input and output value subroutine; 5) the output layer of the module input and output value subroutine; 6) the output layer to the hidden layer subroutine error of generalization; 7) hidden layer to the input layer subroutine error of generalization; 8) the output layer to the third hidden layer Weight adjustment, the output layer threshold adjustment routines; 9) 3rd hidden layer to the second hidden layer weights adjustment, the third hidden layer threshold adjustment routi Platform: |
Size: 11127 |
Author:李洋 |
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Description: This package contains 6 algorithms for training cost-sensitive neural networks. They are over-sampling, under-sampling, threshold-moving, SMOTE and two ensemble methods, i.e. hard-ensemble and soft-ensemble. Platform: |
Size: 37214 |
Author:clatter |
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Description: 基于C开发的三个隐层神经网络，输出权值、阈值文件，训练样本文件，提供如下函数：1）初始化权、阈值子程序；2）第m个学习样本输入子程序；3）第m个样本教师信号子程序；4）隐层各单元输入、输出值子程序；5）输出层各单元输入、输出值子程序；6）输出层至隐层的一般化误差子程序；7）隐层至输入层的一般化误差子程序；8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序；9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序；10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序；11）第一隐层至输入层的权值调整、第一隐层阈值调整计算子程序；12）N个样本的全局误差计算子程序。-C development based on the three hidden layer neural network, the output weights, threshold documents, training sample documents, for the following functions : a) initialization, the threshold subroutine; 2) m learning samples imported subroutine; 3) m samples teachers signal Subroutine ; 4) hidden layer of the module input and output value subroutine; 5) the output layer of the module input and output value subroutine; 6) the output layer to the hidden layer subroutine error of generalization; 7) hidden layer to the input layer subroutine error of generalization; 8) the output layer to the third hidden layer Weight adjustment, the output layer threshold adjustment routines; 9) 3rd hidden layer to the second hidden layer weights adjustment, the third hidden layer threshold adjustment routi Platform: |
Size: 11264 |
Author:李洋 |
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Description: 通过初始化神经网络初值，训练神经网络，得到神经网络权值及阈值-Through the initial value to initialize neural networks, training neural networks, neural networks have been the right value and the threshold Platform: |
Size: 1024 |
Author:mushi |
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Description: bp神经网络源码,这是BP神经网络源码,包括神经网络的创建,权值的初始化和修正,阈值的初始化和修正,权值结果保存-bp neural network source code, which is BP neural network source code, including the creation of neural networks, weights initialization and correction, threshold initialization and amendments, the right to preserve the value of the results Platform: |
Size: 4096 |
Author:刘絮 |
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Description: 神经网络中的权值、阈值以及中间变量很多，但是单片机的程序区和数据区的容量都是有限的。89C52程序区为8K字节，数据区为256K字节。由于探测器受体积限制应尽量避免外扩存储器，所以在实现计算时，要将权值和阈值写成立即数形式减少资源占用。-Neural network weights, threshold and intermediate variables, but procedures for single-chip area and the volume of data are limited. 89C52 process area for 8K byte, 256K-byte data area. As the detector by the volume limit should be avoided as far as possible outside the extended memory, so in the realization of the calculation, the right to the threshold value and written in the form of an immediate reduction in the number of occupied resources. Platform: |
Size: 4096 |
Author:xiawenzhi |
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Description: 神经网络中的权值、阈值以及中间变量很多，但是单片机的程序区和数据区的容量都是有限的。89C52程序区为8K字节，数据区为256K字节。由于探测器受体积限制应尽量避免外扩存储器，所以在实现计算时，要将权值和阈值写成立即数形式减少资源占用。-Neural network weights, threshold and intermediate variables, but procedures for single-chip area and the volume of data are limited. 89C52 process area for 8K byte, 256K-byte data area. As the detector by the volume limit should be avoided as far as possible outside the extended memory, so in the realization of the calculation, the right to the threshold value and written in the form of an immediate reduction in the number of occupied resources. Platform: |
Size: 4096 |
Author:xiawenzhi |
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Description: 神经网络中的权值、阈值以及中间变量很多，但是单片机的程序区和数据区的容量都是有限的。89C52程序区为8K字节，数据区为256K字节。由于探测器受体积限制应尽量避免外扩存储器，所以在实现计算时，要将权值和阈值写成立即数形式减少资源占用。-Neural network weights, threshold and intermediate variables, but procedures for single-chip area and the volume of data are limited. 89C52 process area for 8K byte, 256K-byte data area. As the detector by the volume limit should be avoided as far as possible outside the extended memory, so in the realization of the calculation, the right to the threshold value and written in the form of an immediate reduction in the number of occupied resources. Platform: |
Size: 3072 |
Author:xiawenzhi |
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Description: 针对发票号码进行预处理，定位，阈值分割，然后用神经网络对数字进行识别，-Invoice number for pre-processing, positioning, threshold segmentation, and then use the neural network for digital identification, Platform: |
Size: 8202240 |
Author:徐月琴 |
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Description: ga优化神经网权值&阈值程序。为了方便，我把几个matlab的程序都放在txt文件里，使用时，对照把txt内容复制进matlab即可-ga optimization neural network weights & threshold procedures. For convenience, I have several matlab programs on the txt file, use, control the content of the txt you copied into matlab Platform: |
Size: 1024 |
Author:chengwei |
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Description: 用GA训练BP网络的权值、阈值从而优化神经网络-GA training BP network with the right value, the threshold in order to optimize the neural network Platform: |
Size: 1024 |
Author:sa |
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Description: ga优化神经网权值&阈值程序。
优化的基本原理和过程很多论文可以查到，在此不必赘述我就把用gaot5的小程序贴在下面吧，也是y=1/x-ga optimization neural network weights & threshold procedures. Optimization of the basic principles and processes can be found in many papers, do not have to go into details here I would use a small program gaot5 paste it below as well as y = 1/x Platform: |
Size: 1024 |
Author:肖晔 |
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Description: 神经网络感知器学习规则：Delta学习规则。采用Delta学习规则，进行权值调整，阈值变换函数采用双极性连续函数。程序简单易懂，希望对大家有所帮助！-Neural network perceptron learning rule: Delta learning rule. Delta learning rule used to carry out the right to adjust the value of the threshold bipolar transfer function using a continuous function. Procedures are simple and easy to understand, want to help you! Platform: |
Size: 62464 |
Author:李洋 |
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Description: 用MATLAB实现二层bp神经网络的计算。可以改变阈值和权值以改进算法，并可以将该方法推广到多层网络。-Using MATLAB to achieve the second floor bp neural network computing. Can change the threshold value and weight to improve the algorithm and the method can be extended to the multi-layer network. Platform: |
Size: 3072 |
Author:lyr |
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Description: 这是一个采用遗传算法优化bp神经网络权值阈值的MATLAB程序-This is a genetic algorithm to optimize neural network weights bp threshold MATLAB program Platform: |
Size: 34816 |
Author:chengfei |
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Description: An Improved PSO Algorithm to Optimize BP Neural Network
Abstract
This paper presents a new BP neural network
algorithm which is based on an improved particle swarm
optimization (PSO) algorithm. The improved PSO (which
is called IPSO) algorithm adopts adaptive inertia weight
and acceleration coefficients to significantly improve the
performance of the original PSO algorithm in global
search and fine-tuning of the solutions. This study uses the
IPSO algorithm to optimize authority value and threshold
value of BP nerve network and IPSO-BP neural network
algorithm model has been established. The results
demonstrate that this model has significant advantages
inspect of fast convergence speed, good generalization
ability and not easy to yield minimal local results Platform: |
Size: 252928 |
Author:dasu |
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Description: 网络将输入模式加权求和、与门限比较、再进行非线性运算，得到网络的输出-The network will be weighted sum input mode, compared with the threshold, then a nonlinear operator to get the output of the network Platform: |
Size: 493568 |
Author:徐森 |
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Description: 利用遗传算法优化BP神经网络权值和阈值的matlab-The use of genetic algorithm to optimize BP neural network weights and threshold Platform: |
Size: 111616 |
Author:haiguangchao |
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Search - neural threshold