ISM

使用支持向量机进行特征选择的集成算法模型(Feature selection of support vector machines)

说明：  逻辑混沌映射，通过排列熵算法有效检测出序列的周期——混沌状态(Logical chaotic map, through permutation entropy algorithm to effectively detect the period of the sequence chaotic state)

基于matlab实现了jpg的编码解码，以及有关的分析和操作(Matlab-based codec to achieve a jpg, and the analysis and operation)

移动通信仿真实验，AWGN信道下BPSK调制系统的误码率仿真与分析（验证性），无线通信信道建模，CDMA系统设计与仿真 。(Mobile communications simulation, AWGN channel bit error rate BPSK modulation system simulation and analysis (confirmatory), a wireless communication channel modeling, CDMA system design and simulation.)

实现在加性高斯白噪声下直扩系统的调制与解调(DS-SS modulation)

Implemented methods are: Bisection method The method of the golden section Fibonacci method Development Environment Visual Studio 2010. no comments C / C ++ | Windows NT / 2000 / XP / Vista / 7

NUMERICAL OPTIMIZATION: This is steepest descent method with constant step length to find the minima of f(x, y) = xy exp(− 2x^2 − y^2 + 0.3y) Graphical represxentation in 5 ways of solution, simple and clear explained. (NUMERICAL OPTIMIZATION: This is steepest descent method with constant step length to find the minima of f(x, y) = xy exp(− 2x^2 − y^2+ 0.3y) Graphical represxentation in 5 ways of solution, simple and clear explained. )

说明：  飞行模拟转台伺服系统仿真 S为信号变量 1为正弦 2为阶跃 3为方波(flight simulation turntable servo system simulation S signal to a variable sinusoidal Step 2 for 3 for the square wave)

遗传算法的优化计算，建模自变量降维，matlab经典算法(Genetic Algorithm optimization, modeling arguments dimensionality reduction, matlab classical algorithm)

说明：  遗传算法（Genetic Algorithm，GA）最早是由美国的 John holland于20世纪70年代提出,该算法是根据大自然中生物体进化规律而设计提出的。是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。该算法通过数学的方式,利用计算机仿真运算,将问题的求解过程转换成类似生物进化中的染色体基因的交叉、变异等过程。在求解较为复杂的组合优化问题时,相对一些常规的优化算法,通常能够较快地获得较好的优化结果。遗传算法已被人们广泛地应用于组合优化、机器学习、信号处理、自适应控制和人工生命等领域。(Genetic algorithm (GA) was first proposed by John Holland of the United States in the 1970s. It is designed and proposed according to the evolution law of organisms in nature. It is a computational model of biological evolution process simulating natural selection and genetic mechanism of Darwin's biological evolution theory. It is a method to search the optimal solution by simulating natural evolution process. The algorithm transforms the solving process of the problem into a process similar to the crossover and mutation of chromosome genes in biological evolution by means of mathematical method and computer simulation. When solving complex combinatorial optimization problems, compared with some conventional optimization algorithms, it usually can get better optimization results quickly. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life.)