搜索资源列表
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使用MATLAB语言实现BP神经网络的分类功能,代码很简洁-using MATLAB BP neural network classification function, a very simple code
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matlab平台的基于bp神经网络的遥感图像分类,matlab platform bp neural network-based remote sensing image classification
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基于BP神经网络的遥感图像分类代码。从样本中提取崇明岛东滩十种地物的光谱特征,并训练BP网络,再利用网络进行分类-BP neural network-based remote sensing image classification code. Extracted from samples of 10 kinds of Chongming Island, Dongtan features of the spectral characteristics and to train BP networ
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使用MATLAB GUI编写的有界面的神经网络分类的方法。适合做课程设计-The preparation of the use of MATLAB GUI interface of the method of neural network classification. Suitable for curriculum design
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模式识别的
图像的贝叶斯分类
图像的贝叶斯分类
神经网络模式识别-Bayesian pattern recognition image classification image of the Bayesian classifier neural network pattern recognition
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利用物体的几何特征参数,和bp神经网络实现分类。-The use of the geometric characteristics of object parameters, and the realization of bp neural network classification.
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神经网络源码,可应用于遥感图像的分类,采用的包括bp、kohonen。可以作为范例来学习。-Neural network source code can be used in remote sensing image classification, using the included bp, kohonen. Can serve as examples to learn.
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用Matlab编写的神经元网络程序,可对图像进行分类。程序用到了4种神经元方法,BP效果最好。-Neural network using Matlab program, the image classification. The program used four kinds neurons BP effect.
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基于matlab R2010a的BP神经网络在遥感图像分类中的应用源代码-Application source code matlab R2010a BP neural network-based remote sensing image classification
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基于matlab神经网络的遥感图像分类,使用了BP神经网络-Matlab neural network-based remote sensing image classification using BP neural network
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使用matlab语言,实现了BP神经网络分类的精度验证评价-Matlab language BP neural network classification accuracy validation evaluation
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用于植物叶片分类的图形界面,是Matlab源码,用BP神经网络实现-For plant leaf classification graphical interface, the Matlab source code, using BP neural network
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利用卷积神经网络进行MINIST数据集的分类识别,MATLAB源程序。-Convolution neural network classification MNIST dataset, MATLAB source.
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主要是BP神经网络的分类算法,采用MATLAB编写,可以成功运行-Mainly BP neural network classification algorithm, using MATLAB prepared, can be successfully run
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BP神经网络基本原理概述:这种网络模型利用误差反向传播训练算法模型,能够很好地解决多层网络中隐含层神经元连接权值系数的学习问题,它的特点是信号前向传播、误差反向传播,简称BP(Back Propagation)神经网络。BP学习算法的基本原理是梯度最快下降法,即通过调整权值使网络总误差最小,在信号前向传播阶段,输入信号经输入层处理再经隐含层处理最后传向输出层处理;在误差反向传播阶段,将输出层输出的信号值与期望输出信号值比较得到误差,若误差较大则把误差信号传回隐含层直至输入层,在各层神经元中使用
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