wugongyouhua

利用快速傅立叶变换,在频域中实现脉冲压缩的matlab仿真程序()

This document describe matlab computing environment: Engineering, Technical Computing,Files and File Management,Mathematical Functions and Applications,DataAnalysis and other.

近红外光谱分析中的BP模型建立过程，其中在原有BP神经网络模型的基础上自己编程加入了近红外模型验证系数的计算，如R-SquareC.R-SquareP,RMSEC,RMSEP,RSD等。并加入循环语句，设定区间程序可以自动运行并判断最佳隐含层结点数，最佳学习速率，动量因子，训练次数等，同时加入一个验证校正模型实例。(The process of BP model in near infrared spectrum analysis, including the original BP neural network model on the basis of their programming joined the nir model validation coefficient calculation, such as R- SquareC. R- SquareP, RMSEC, RMSEP, RSD, etc. And join the loop statement, set interval program can be run automatically and determine the best hidden layer nodes, the optimal learning rate, momentum factor, the number of training, etc. )

matlab英文教材：matlab在应用数值方法的应用(English teaching materials matlab: matlab application in the application of numerical methods)

自适应滤波器的lms和Rls算法的源码及收敛过程仿真(Lms adaptive filter algorithms, and Rls source and the convergence process simulation)

find probability of 1 vector

说明：  通过分析交通流量时间序列的特点，引入BP神经网络进行短时交通流预测。首先，分析了短时交通流量预测的意义及研究背景；然后，介绍了BP神经网络的结构模型、学习规则以及BP算法的改进算法；最后，通过BP神经网络对短时交通流进行预测，并分析了在各种不同条件下的预测情况。(through the analysis of the characteristics of traffic flow time series, introduces BP neural network for short-term traffic flow prediction. First of all, it analyzes the significance of short-term traffic flow prediction and research background. And then, it has discussed the model and structure, the study rule and the improved algorithm of BP neural network. Finally, by BP neural network to forecast the short-term traffic flow, it analyzes under different conditions of the neural network prediction.)

完整的Kalman Filter、EKF、IEKF、Unscented Kalman Filter及Particle Filter滤波程序。(The complete Kalman Filter, EKF, IEKF, Unscented Kalman Filter and Particle Filter filtering procedure.)

VLE calculation - scmr

目动曝光控制参数的获得方法及控制方法和成像装置--专利(Project AE acquisition method and control method and control parameters of the imaging device)