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.
fa2
- 神经网络模拟正弦函数演示-neural network simulation sine function demo
switchtest
- 内容:simulink 非线性模块仿真正弦信号通过新回环函数，switchtest.fig, switchtest.mdl
RBFwangluo
- RBF网络逼近函数密密麻麻，该源程序能够无限逼近任意函数,例程中为逼近正弦函数，误差非常小。
用M函数编写的神经网络PID算法的例程
- 用M函数编写的神经网络PID算法的例程，可实现对阶跃、正弦波的很好跟踪，没有超调。-Written by M function neural network PID algorithm routines, can be realized step, a good sine wave tracking, no overshoot.
ANN
- 实验使用BP神经网络来逼近一个较复杂的正弦函数，并观察BP神经网络的各个参数对BP神经网络的影响.-Experimental use of BP neural network to approximate a more complex sine function, and to observe the parameters of BP neural network on the impact of BP neural network.
NNapply1
- 利用线性神经网络对某一正弦信号进行线性预测。利用函数newlind设计线性神经网络， 在已知正弦信号过去5个值得情况下，预测其将来值。 定义需要的信号，共持续5s，采样频率40Hz-Using linear neural network to a sinusoidal signal for linear prediction. Newlind design using a linear function of neural networks, known sinusoidal
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
Replaceneuralnetwork
- 替换小波神经网络程序：隐层函数不用小波，用一个余弦函数或正弦函数。这个程序见附录。网络的输出也是不错的，只是有时误差曲线有点波动，但不影响系统输出结果。-Replace the neural network program
bp
- 通过BP神经网络算法对一个正弦函数进行实验操作，并绘出实验结果图-BP neural network algorithm by a sine function of the experimental operation, and draw the map results
NEW
- 基于四元数法的捷联惯性导航系统姿态解算及正弦输入函数的生成-Quaternion-based strapdown inertial navigation system attitude solution and the generation of sinusoidal input function
cmac
- MATLAB语言作为编程工具构造CMAC神经网络，利用公式Wij(k+1)=Wij(k)+β(yid-yi)α/αTα对连接权系数Wij进行调整，用来对正弦函数sin（x）进行逼近-MATLAB programming language as a tool to construct CMAC neural network, using the formula Wij (k+1) = Wij (k)+ β (yid-yi) α/αTα the connection weights Wij to a
Function-approximation
- 应用神经网络来进行正弦函数的逼近，通过参数的调整，效果会有所变化-Neural network to approximate the sine function by parameter tuning, the effect will vary
bp
- 使用BP神经网络算法对一个函数实现跟踪（如正弦函数）-The BP neural network algorithm to achieve tracking of a function (such as the sine function)
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.
bp
- 基于附加动量的神经网络，用它来模拟正弦函数-Additional momentum-based neural network, and use it to simulate a sine function
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
sin_bianshi
- 采用BP神经网络进行了非线性正弦信号的辨识，采用双曲函数为激发函数-Nonlinear sine signal is identified with BP network,and it uses the hyperbolic function as the excitation function.
BP
- 使用C语言模拟实现bp神经网络逼近正弦函数-Using C language to achieve BP network approximation analog sine function
CMAC1
- 利用小脑模型神经网络对函数进行逼近，逼近函数为正弦函数。-The function is approximated by the cerebellar model neural network, and the function is the sine function.