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单输出神经网络拟合如下函数：y=sinx1+xinx2+sinx3+sinx4，变量取值范围=[2,2PI]-single-output neural network function fitting as follows : y = sinx1 xinx2 sinx3 sinx4, variable value in the range = [2,2 PI]
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神经网络训练，应用matlab7NN包，用一个隐藏层使用5折交叉验证。-Training the Neural Network
This scr ipt is something that I did for a course at Uni. It uses the Neural Networking package provided with MatLab 7 unfortunately I m not sure if it s available with the earlier ve
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一种基于模糊神经网络的控制策略，融合了模糊控制和神经网络的优点，根据变量的动态调整实施控制器的动态参数调整-Based on fuzzy neural network control strategy, the convergence of fuzzy control and neural network advantages, in accordance with the dynamic adjustment of the implementation of variable controll
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用BP神经网络实现模糊控制规则为T=int[(e
+ec)/2]的模糊神经网络控制器。可以改变隐层节点数和学习速率。网络训练算法是变学习速率法。-BP neural network with fuzzy control rules for the T = int [(e+ Ec)/2] of the fuzzy neural network controller. Can change the hidden layer nodes and learning rate. Network tra
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有关变结构神经网络的资料,希望对大家有用!-Variable structure neural network-related information useful for all of us hope!
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采用将BP神经网络的学习算法应用于PID控制中，使BP神经网络与PID控制算法结合起来，通过吸收两者的优势，使系统具有自适应性。这样系统可自动调节控制参数，更好地适应输入变量的变化，提高控制性能和可靠性。本文从BP神经网络的基本构成原理、学习规则和学习算法出发，设计了基于BP神经网络的PID控制器，并对其进行了仿真分析,结果表明，该控制方案可行、有效。-We apply the learning algorithm of BP neural network to the PID control,
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用神经网络变步长算法实现模式识别，用C++实现-With variable step-size neural network pattern recognition algorithm, using C++ to achieve
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神经网络求权重源码，输入多个自变量和一个因变量，可以得到与因变量关系显著的几个自变量。-Weight neural network source code request, enter the number of independent variables and a dependent variable, can be a significant relationship with the dependent variable, several independent variables.
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多变量pid神经网络控制仿真程序，用于多变量时的控制-Neural network control of multivariable pid simulation program for multi-variable control
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结合BP神经网络应用平均影响值（MIV，Mean Impact Value）方法来说明如何使用神经网络来筛选变量，找到对结果有较大影响的输入项，继而实现使用神经网络进行变量筛选。-BP neural network application with the average impact value (MIV, Mean Impact Value) method to illustrate how to use the neural network to filter the variables
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BP 神经网络用于预测 使用平台 - Matlab7.0数据为1986年到2000年的交通量 ，网络为3输入，1输出
15组数据，其中9组为正常训练数据，3组为变量数据，3组为测试数据-BP neural network to predict the use of platforms- Matlab7.0 data for 1986 to 2000, traffic volume, network, 3 inputs, 1 output 15 sets of data, of which
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基于BP神经网络的数字图像识别
采用引进动量项和变步长法的改进型BP网络
train.m 训练
shibie.m 识别
actfun.m 激活函数
image.rar 图像库
-BP neural network-based digital image recognition and momentum term with the introduction of variable step method of improved BP network train.m trai
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神经网络变量筛选—基于BP的神经网络变量筛选。-Neural network variable selection- based on BP neural network variable selection.
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此程序解决了人工神经网络变量的筛选问题，精确地选出适合的变量-This procedure of artificial neural network to solve the screening problem of variable
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此程序解决人工神经网络的筛选，能精确地选出合适的变量-This resolves the artificial neural network screening, can accurately select the appropriate variable
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此程序解决人工神经网络的筛选，能精确地选出合适的变量-This resolves the artificial neural network screening, can accurately select the appropriate variable
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此程序解决人工神经网络的筛选，能精确地选出合适的变量-This resolves the artificial neural network screening, can accurately select the appropriate variable
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此程序解决人工神经网络的筛选，能精确地选出合适的变量-This resolves the artificial neural network screening, can accurately select the appropriate variable
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设计并训练三种神经网络使之分别逼近下列函数，精度Sm偏差小于20，Ry偏差小于1.5。 (各变量取值范围： =20～90, =35～55,a1=3～13, a2=0.3～3,Sd=0.05～0.45, =0.05～0.04,L2=0.015～0.06,T=7～110)-Design and training of three neural networks respectively approximation of the following function, precision Sm dev
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神经网络变量筛选—基于BP的神经网络变量筛选-Neural network variable selection- BP neural network variables based screening
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