DOA

说明：  RNA二级结构预测，比对，结构构象比对以及聚类。(RNA secondary structure prediction, comparison, structural conformation and clustering than.)

通信里的2ASK信号的产生及分析，很不错，经典实用(Communication in the 2ASK signal generation and analysis, very good, classic and practical)

三阶龙格库塔，四阶龙格库塔程序，程序完善，通用性强！(The third order runge kutta, fourth order runge kutta procedures, process improvement, strong commonality. )

多点定位TDOA的chan算法,和坐标转换，经纬度转换成平面直角坐标系(chan algorism altitude longtitude xy coordinates)

说明：  MATLAB 代码 第1章 BP神经网络的数据分类——语音特征信号分类 第2章 BP神经网络的非线性系统建模——非线性函数拟合 第3章 遗传算法优化BP神经网络——非线性函数拟合 第4章 神经网络遗传算法函数极值寻优——非线性函数极值寻优 第5章 基于BP_Adaboost的强分类器设计——公司财务预警建模 第6章 PID神经元网络解耦控制算法——多变量系统控制 第7章 RBF网络的回归--非线性函数回归的实现 ....等58章(MATLAB code The first chapter is BP neural network data classification -- speech characteristic signal classification The second chapter is the nonlinear system modeling of BP neural network nonlinear function fitting The third chapter, genetic algorithm optimization BP neural network - nonlinear function fitting The fourth chapter, neural network, genetic algorithm, function extreme value optimization nonlinear function extremum seeking The fifth chapter is based on BP_Adaboost's strong classifier design -- the company financial early-warning model The sixth chapter is PID neural network decoupling control algorithm multivariable system control The seventh chapter is the regression of RBF network the realization of nonlinear function regression .........the last is 58 chapters)

java与matlab编程的例题。想要做类似工程项目的人员可以参考哦。(java and matlab programming examples. Want to do similar projects can reference Oh.)

说明：  盲源分离FastICA、matalab程序，(FAST KERNEL ICA | |--------------------| Version 1.0- February 2007 Copyright 2007 Stefanie Jegelka, Hao Shen, Arthur Gretton This package contains a Matlab implementation of the Fast Kernel ICA algorithm as described in [1]. Kernel ICA is based on minimizing a kernel measure of statistical independence, namely the Hilbert-Schmidt norm of the covariance operator in feature space (see [3]: this is called HSIC). Given an (n x m) matrix W of n samples from m mixed sources, the goal is to find a demixing matrix X such that the dependence between the estimated unmixed sources X *W is minimal. FastKICA uses an approximate Newton method to perfom this optimization. For more information on the algorithm, read [1], and for more information on HSIC, refer to [3]. The functions chol_gauss and amariD are taken from and based on, respectively, code from Francis Bach (available at http://cmm.ensmp.fr/~bach/kernel-ica/index.htm). The derivative is com)

恒虚警检查，根据虚警率确定门限值，进而确定检测概率(CFAR check, according to the false alarm rate to determine the threshold, and to determine the probability of detection)

由惯性器件的输出解算出飞行器的位置、速度、姿态信息(The output from the inertial device solves for the aircraft position, speed, attitude information)

wind power systems with permanent magnet generator in matlab