TEMforward

Multilayer Perceptron

matlab wave matlab wave matlab wave matlab wave

求方程的根植，，仅供参考，仅供参考，仅供参考，仅供参考(Demand equation is rooted, for reference, reference, reference, reference)

Documentation om TI TWL3014 Iota Analog Baseband

matlab在自动控制原理的的应用，ppt讲解格式，详细，并且配有例题的源代码，面向实际，很适合解决自动控制原理方面的问题(Matlab in control of the application of the principle, ppt format to explain in detail, Examples and equipped with the source code, to face reality, the solution is very suitable for the automatic control theory problems)

rbf神经网络源代码，基于MATLAB编程(rbf neural network, based on MATLAB programming)

调频系统仿真实例—调频系统仿真模块源代码(FM system simulation- simulation module source code modulation system)

MEDLL算法多径参数估计，2012b上可以运行，添加了高斯白噪声； 应用于多径检测和抗多径； 函数的输入是相关峰采样；(MEDLL Multipath parameter estimation algorithm can be run on the 2012b, add Gaussian white noise used in multipath detection and anti-multipath input function is the correlation peak sampling )

一个用卡尔曼滤波故障检测的matlab例子(Examples of Kalman Filter)

城市交通流的运行存在着高度的复杂性、时变性和随机性,实时准确的交通流预测是智能交通系统,特别是先进的交通管理系统与先进的出行者信息系统研究的关键. 基于交通流预测的特点,给出了基于遗传算法的小波神经 网络的交通预测模型GA-WNN ,用具有自然进化规律的遗传算法来对小波神经网络的连接权值和伸缩平移尺度进行前期优化训练,部分代替了小波框架神经网络中按单一梯度方向进行参数优化的梯度下降法,克服了单一梯度下降法易陷入局部极小和引起振荡效应等缺陷. 仿真实验验证了GA-WNN 预测模型对短时交通流的预测的有效性.(For the high complexity ,time-variation and probability of urban traffic flow , its real-time and exact prediction is critical to the research of intelligent traffic systems , especially for the advanced traffic manage-ment system and advanced traveler information system. Based on the character of the traffic flow prediction , a GA-WNN model is given based on the wavelet neural network with genetic algorithm. The genetic algorithm of natural evolving law for the gradient descendent algorithm in Wavelet Neural Network is partly substituted to pre-optimize the connection weight and the extension scale of the wavelet neural network and later optimize the parameters along a single gradient vector. This method overcomes some drawbacks when there exists a single gradient descendent algorithm , such as local minimum and oscillation. A short-time traffic flow prediction sim- ulation using the GA2WNN prediction model demonstrates the validity of the model .)