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用BP网络完成函数的逼近源程序.zip
- 用BP网络完成函数的逼近源程序
人工神经网络BP算法_函数逼近
- 用三层BP网络实现一个单输入单输出函数的逼近,文件中一个CPP文件和一个WORD文件,其中word文档中有对算法理论,算法设计,程序结果及改进方法试验的详细说明-using neural networks to achieve a single-input single-output function approximation, a CPP paper documents and a Word document, which document a word of the algorithm t
mat-bp
- 用BP实现函数逼近 Matlab有如下特点: 1.编程效率高 例如:普通的矩阵计算用一般的高级语言,如C,Pascal等,需要十几至几十行语句,用matlab, 至多几行。 2.用户使用方便; 3.语句简单,内涵丰富; 4.高效方便的矩阵和数值计算; 5.方便的绘图功能,等等。
BP
- 使用BP网络逼近非线性函数,可以获得很不错的逼近效果!
三层BP神经网络逼近非线性函数
- 内容如题,其中BP神经网络的建立采用自编函数,而非Matlab自带的神经网络建立函数
BP_tanh_linaer
- BP神经网络Simulink模型。。例子给了个离散传递函数。训练后的网络可以逼近任意传递函数,或者非线性函数。-Simulink model of BP neural network. . Examples for the discrete transfer function. Trained network can approximate any transfer function, or the nonlinear function.
BP
- 应用BP神经网络对两个函数进行非线性逼近,并给出MATLAB源程序,还对结果进行了分析。-Application of BP neural network of the two non-linear function approximation, and gives MATLAB source code, but also on the results are analyzed.
bp
- 基于BP神经网络算法的函数逼近,利用matlab实现BP算法逼近任意非线性函数-BP neural network algorithm based on function approximation, using matlab to achieve BP algorithm approximate any nonlinear function
BP
- 不使用matlab神经网络工具箱用bp网络实现函数逼近。利于了解bp网络的原理-Do not use matlab neural network toolbox with bp networks function approximation. Help to understand the principle of the network bp
BP
- 利用BP网络实现函数逼近,本程序以cos(k*pi*p)为例进行逼近-Function approximation using BP network, the procedures to cos (k* pi* p) as an example approximation
bp-rbf-neural-networks
- 介绍如何通过matlab使用bp神经网络和rbf神经网络来逼近非线性函数-Describes how to use matlab bp neural network and rbf neural networks to approximate nonlinear functions
mat121
- 用工具箱实现正弦函数的逼近,BP逼近与RBF的逼近
BP神经网络
- 利用BP神经网络去做函数逼近和解决分类问题(BP neural network is used to do function approximation and solve classification problem)
BP
- BP神经网络经过训练与学习,逼近非线性函数(BP neural networks are trained and studied to approximate nonlinear functions)
BP神经网络逼近(matlab程序)
- Bp神经网络MATLAB小程序,相当实用,推荐实用(Neurofuzzy design and model construction of nonlinear dynamical processes from data)
BP
- 利用BP网络逼近对象y(k)=u(k)^3+y(k-1)/(1+y(k-1)^2)。采样时间取1ms。输入信号为u(k)=0.5sin(6*pi*t)。(Approximate object y (k), =u (k), ^3+y (k-1) / (1+y (k-1) ^2) using BP networks. Sampling time is 1ms. The input signal is u (k) =0.5sin (6*pi*t).)
BP
- 用代码(非工具箱)实现BP神经网络对含有随机噪声的sin函数的逼近(The approximation of sin function with random noise by BP neural network with code (non toolbox))
BP,RBF
- BP神经网络作为一种前馈性的神经网络,RBF神经网络由于其独特的联想记忆功能,常常用来用于识别和优化计算方问题上。分别对这两种算法用于对逼近非线性函数进行编程,观察其拟合情况后,用其他未训练的样本数据进行泛化能力分析。(BP neural network is a feed-forward neural network. RBF neural network is often used to identify and optimize the computation problem due to
pso-bp
- 使用粒子群算法优化bp神经网络,完成函数逼近(Optimize bp neural network using particle swarm optimization algorithm to complete function approximation)