搜索资源列表
u01
- 正弦函数拟合，各种BP神经网络算法的应用。-sine function fitting, various BP neural network algorithm application.
bys
- 采用贝叶斯正则化算法提高BP网络的推广能力。在本例中，将采用两种训练方法，即L-M优化算法（trainlm)和贝叶斯正则化算法（trainbr),用以训练BP网络，使其能够拟合某一附加有白噪声的正弦样本数据。-The use of Bayesian regularization algorithm for BP network to improve generalization ability. In this case, two types of training methods will b
bp
- 用bp算法拟合正弦曲线，并采集数据。其基本思想是梯度下降法。-Sine curve fitting algorithm with a bp, and collecting data. The basic idea is the gradient descent method.
LNN
- 线性神经网络对曲线进行预测和拟合，可对正弦余弦等曲线进行任意步长和拟合。没用到神经网络工具箱。-Linear neural network prediction and curve fitting, can be arbitrary sine cosine curves such as step length and fitting. Useless to the neural network toolbox.
sinPolyfit
- Matlab 实现多项式曲线拟合（正弦曲线），交叉验证（十折交叉验证）-poly fit ，cross validation
BP1
- 采用L-M优化算法用以训练BP神经网络，使其能够拟合某一附加有白噪声的正弦样本数据。设计一个三层BP神经网络，其网络的隐含层神经元的激励函数为双曲正切型，输出层各神经元的激励函数为线性函数，隐含层有6个神经元-LM optimization algorithm used to train BP neural network to enable it to fit a white noise, sine additional sample data. Design a three BP neura
BP
- 本程序为基于matlab的BP神经网络应用实例——正弦函数拟合-Sine function fitting
nihe
- 1. 分别用2,3,4,5阶多项式来逼近[0,3]上的正弦函数sinx，并作出拟合曲线及sinx函数曲线线图。了解多项式的逼近程度和有效拟合区间随多项式的阶数有何变化。-1. Were treated with 2,3,4,5-order polynomial approximation [0,3] The sine function sinx, and make sinx function curve fitting curve and line graphs. Understand the
task4
- 频率辨识开环系统传递函数，传递函数为 ，试通过正弦扫频及Bode图拟合来辨识该传递函数，写出其设计思想和详细推导过程，并给出仿真程序和仿真结果-Open-loop frequency identification system transfer function, transfer function, the sine sweep test and the Bode plot by fitting to identify the transfer function, write its desi
myamac
- 用小脑神经网络进行正弦函数拟合，并没有用到该进的小脑神经网络哦，用的是matlab编程-I use CMAC to fit the sin function curve.the effect is not so good ,because I don t use the enhanced algorithm of CMAC.If you have any problem ,you are welcome to tell me.
Matlab-so-called-brann
- 用以训练 BP 网络，使其能够拟合某一附加有白噪声的正弦样本数据-train bp network, to fit a noisy-free sin sample data
BP
- BP神经网络 实现的是正弦函数用BP神经网络的拟合，探讨了不同神经元个数对拟合的影响-BP
Bayes-in-BP(code)
- 采用贝叶斯正则化算法提高 BP 网络的推广能力。在本例中，我们采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正 则化算法（trainbr），用以训练 BP 网络，使其能够拟合某一附加有白噪声的正弦样本数据。-Use Bayes to train BP network
bpsin
- 设计BP神经网络输入、输出和隐含层，拟合输出正弦曲线-Design of BP neural network input, output and hidden layer, and fitting the output sine curve
zhengxianchazhi
- 正弦插值的代码,用来拟合曲线插值，可以直接使用-sinx/x function
bpNeural-network-instance
- 例1 采用动量梯度下降算法训练 BP 网络。 例2 采用贝叶斯正则化算法提高 BP 网络的推广能力。在本例中，我们采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正则化算法（trainbr），用以训练 BP 网络，使其能够拟合某一附加有白噪声的正弦样本数据。-Example 1 uses the momentum gradient descent algorithm to train the BP network. Example 2 uses the Bayesian
sjwl
- 用matlab编程，运用BP网络拟合白噪声的正弦样本数据(Using BP network to fit sine sampling data of white noise)
SimpleBPNN
- 神经网络bp模型拟合正弦函数，神经网络例子(Neural network bp model fitting sine function)
sintest
- 实现对正弦波形进行AD采样，并做幅值和频率的拟合。(The sine wave is sampled by AD, and the amplitude and frequency are fitted.)