Description: 基于GMM的概率神经网络PNN具有良好的泛化能力，快速的学习能力，易于在线更新,并具有统计学的贝叶斯估计理论基础，已成为一种解决像说话人识别、文字识别、医疗图像识别、卫星云图识别等许多实际困难分类问题的很有效的工具。而且PNN不但具有GMM的大部分优点，还具有许多GMM没有的优点，如强鲁棒性，需要更少的训练语料，可以和其他网络其他理论无缝整合等。-GMM based probabilistic neural network PNN good generalization ability, the ability to learn fast, easy online updates, and with the Bayesian statistical theory based on estimates, and has become a solution as speaker recognition, text recognition, medical image recognition, satellite images and other real recognition when difficulties classification of very effective tool. But GMM PNN is not only the most advantages, but also has many advantages GMM not as strong robustness, require less training corpus, and other networks to other theories, such as seamless integration. Platform: |
Size: 7158 |
Author:姜正茂 |
Hits:
Description: 几篇关于神经网络泛化的论文.几篇关于神经网络泛化的论文-several neural network generalization of the papers. Several of the generalization of the neural network papers Platform: |
Size: 633517 |
Author:曾志伟 |
Hits:
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 |
Hits:
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 |
Hits:
Description: 基于GMM的概率神经网络PNN具有良好的泛化能力，快速的学习能力，易于在线更新,并具有统计学的贝叶斯估计理论基础，已成为一种解决像说话人识别、文字识别、医疗图像识别、卫星云图识别等许多实际困难分类问题的很有效的工具。而且PNN不但具有GMM的大部分优点，还具有许多GMM没有的优点，如强鲁棒性，需要更少的训练语料，可以和其他网络其他理论无缝整合等。-GMM based probabilistic neural network PNN good generalization ability, the ability to learn fast, easy online updates, and with the Bayesian statistical theory based on estimates, and has become a solution as speaker recognition, text recognition, medical image recognition, satellite images and other real recognition when difficulties classification of very effective tool. But GMM PNN is not only the most advantages, but also has many advantages GMM not as strong robustness, require less training corpus, and other networks to other theories, such as seamless integration. Platform: |
Size: 7168 |
Author:姜正茂 |
Hits:
Description: 几篇关于神经网络泛化的论文.几篇关于神经网络泛化的论文-several neural network generalization of the papers. Several of the generalization of the neural network papers Platform: |
Size: 632832 |
Author:曾志伟 |
Hits:
Description: 递归神经网络程序，用于递归神经网络的训练，并可以进行泛化。-Recurrent neural network procedure for recurrent neural network training and generalization can be carried out. Platform: |
Size: 1024 |
Author:杨丽 |
Hits:
Description: 小波神经网络程序，用于小波神经网络的训练及泛化求解。-Wavelet neural network procedure for wavelet neural network for solving the training and generalization. Platform: |
Size: 2048 |
Author:杨丽 |
Hits:
Description: rbf神经网络应用于系统辨识,比BP网络具有较好的泛化能力,学习速度快,辨识效果好!-rbf neural network applied to system identification, than the BP network has better generalization ability, learning speed and better recognition! Platform: |
Size: 1024 |
Author:liyan |
Hits:
Description: 人工神经网络(ANN)的泛化特性是神经网络最重要的特性,同时也是最不容易保证的特性。本程序对改进泛化的神经网络算法以及新兴的机器学习算法——支持向量机算法进行研究,-Artificial Neural Network (ANN) the generalization characteristics of neural networks are the most important characteristics, but also not easy to guarantee the most features. This procedure for improving the generalization of neural network algorithm, as well as the emerging machine learning algorithms- Support Vector Machine algorithm research, Platform: |
Size: 7168 |
Author:王旭 |
Hits:
Description: 支持向量机和BP神经网络虽然都可以用来做非线性回归，但它们所基于的理论基础不同，回归的机理也不相同。支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。为了验证这种观点，本文编写了支持向量机非线性回归的通用Matlab程序和基于神经网络工具箱的BP神经网络仿真模块，仿真结果证实，支持向量机做非线性回归不仅泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。
-Support Vector Machine and BP neural network, even though there can be used to make non-linear regression, but they are based on the theoretical basis for the different, the mechanism of regression is not the same. Support vector machine based on structural risk minimization theory, generally considered the generalization ability of neural networks than the strong. To test this view, the paper prepared by non-linear regression support vector machine procedures and based on a common Matlab neural network toolbox of BP neural network Platform: |
Size: 3072 |
Author:孙准 |
Hits:
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 |
Hits:
Description: 为了更好了解遗传神经网络在系统中的对比效果，我们以室内温度控制为例，设室内的温度控制目标为18℃（20℃），其他参数保持不变的情况下，分别按照单独神经网络和基于遗传算法优化后的神经网络控制进行仿真实验.仿真结果表明，上述应用遗传算法优化的神经网络是非常有效的，通过运用遗传算法对神经网络进行优化，使其具有良好的泛化能力和快速的收敛性。-To better understand the genetic neural network contrast in the system, we control the indoor temperature, for example, set the room temperature control goal is 18 ℃ (20 ℃), other parameters remaining unchanged, respectively, according to a separate neural networks optimized based on genetic algorithm and neural network control simulation. The simulation results show that the genetic algorithm to optimize the neural network is very effective, through the use of genetic algorithms to optimize the neural network, it has a good generalization ability and fast convergence. Platform: |
Size: 19456 |
Author: |
Hits:
Description: 摘 要： 介绍BP算法神经网络由线拟舍方法，并借助MATLAB工具箱函数将它运用于方解石色散特性研
究，通过拟合效果图，误差曲线，误差范数反映BP神经网络的优越性，体现BP算法较高的预测能力和良好的泛化能
力，并且可以自动地确定数学模型．精确度高，原理也较简单，尤其对复杂的输入输出系统具有更好的效果。-Abstract： Curve fitting method of BP neural network was introduced and applied in the model of the dispersion of calcite
crystals by MATLAB tools．The results show that BP algorithm has high forecasting capacity and good generalization capacity
in three areas：the map of curve fitting，the deviation curve and the error norm．BP neural network can automatically identify
mathematical model，which has higher precision，and its principle is relatively simple．So it is a very good tool for complex
input-output system． Platform: |
Size: 293888 |
Author:zhenzhen |
Hits:
Description: 遗传算法与BP神经网络的结合，实现了有效权值的确定和泛化能力的提高-The combination of genetic algorithm and BP neural network, to identify and improve generalization ability of the effective weights Platform: |
Size: 1024 |
Author:lhuacheng |
Hits:
Description: 研究并分析了B P神经网络的结构和特点，针对不足之处提出改进方法。在改进的基础
上建立神经网络软件可靠性新模型。通过MATL AB仿真工具进行了实例仿真，证实该新模型比传统
模型预测精度高，泛化能力强-Research and analysis of the structure and characteristics of BP neural network, an improved method for the shortcomings. In the modified base
The establishment of a new model of neural network software reliability. By MATL AB simulation tool was simulated, confirmed that the new model than the traditional
Model predicts high precision, strong generalization ability Platform: |
Size: 603136 |
Author:李云 |
Hits:
Description: 几篇关于神经网络泛化的论文,几篇关于神经网络泛化的论文 不错的-A few paper on neural network generalization, a few good paper on neural network generalization Platform: |
Size: 636928 |
Author:seffcur |
Hits:
Search - neural network generalization