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说明：  Binary Genetic Algorithm

EKF—SLAM 机器人定位和地图创建 教程是关于SLAM的系列教程，从网上下载，本人觉得讲解非常详细，并配有习题供动手实践， 非常适合初学者学习使用。本人学习后，实现了课后作业的所有程序，一并打包上传，供大家共 同学习探讨(EKF—SLAM )

Saleh model for use in memoryless nonlinear power amplifier (PA) behavioural modelling

地震波阻抗反演，用于一维、二维褶积模型，其中梯度函数和目标函数根据自己的需要建立(Seismic impedance inversion, for one-dimensional, two-dimensional convolution model, in which the objective function and the gradient function according to their own needs to establish)

数学建模书上的答案，第三版的课后习题答案，希望你们可以下载查看。(Mathematical modeling books the answer to the third edition of after-school Exercise answers, I hope you can download and view.)

matlab的经典教学案例，初学者的最好资料，来看看吧。(matlab classic teaching case, the best information for beginners, have a look.)

关于时间序列分析的matlab程序代码，对原有的工具箱的补充(on time series analysis Matlab code, the original supplementary Toolbox)

分布式功率控制算法，对研究CDMA功率控制很有帮助~(Distributed Power Control)

约束优化问题： minRosen(Rosen梯度法求解约束多维函数的极值)(算法还有bug) minPF(外点罚函数法解线性等式约束) minGeneralPF(外点罚函数法解一般等式约束) minNF(内点罚函数法) minMixFun(混合罚函数法) minJSMixFun(混合罚函数加速法) minFactor(乘子法) minconPS(坐标轮换法)(算法还有bug) minconSimpSearch(复合形法)(Constrained optimization problem: minRosen (Rosen gradient method for solving constrained multi-dimensional function of extremes) (algorithm also bug) minPF (solving linear equations outside the penalty function method constraints) minGeneralPF (outside the penalty function method for solving general equality constraints) minNF (internal penalty function method) minMixFun (mixed penalty function method) minJSMixFun (mixed penalty function acceleration method) minFactor (multiplier method) minconPS (coordinate rotation method) (algorithm also bug) minconSimpSearch (complex method))

This file compares three different versions of the Kalman filter. The Kalman filter is used for recursive parameter estimation. The Kalman filter can handle noisy measurements. The first implemented filter (fcn_KF) is the Kalman filter with standard update of the covariance matrix P. The covariance matrix reflects the uncertainties of the predictions. To improve the numerical stability Potter developed a square root update (fcn_KF_SRP) of the covariance matrix P. Another version is the square root covariance update via triangularization (fcn_KF_SRT). This file generates a model. Then the three Kalman filters perform an estimation of the model parameter. At the end the results are compared. Sources: Simon, D. (2006): Optimal state estimation Kaminski, P. (1971): Discrete Square Root Filtering: A Survey of Current Techniques Golub, G. (1996): Matrix Computations