Wiener111111

m file code draw square adjustable

C-CMethod.m C-C法主函数，调用方式见函数体 correlation_integral.m 关联积分求解（被C-C法主函数调用） disjoint.m 将时间序列分为t个不相交的向量 heaviside.m 判断函数>0为1，<0为0 reconstitution.m 相空间重构函数 lorenz_phase.m 实验函数，用lorenz系统测试C-C法 lorenz_sim.mdl 用simulink建立的lorenz系统(C-CMethod.m CC method main function, see the body of the function invocation correlation_integral.m correlation integral solution (CC method is the primary function call) disjoint.m time series is divided into t disjoint vector heaviside.m determine the function> 0 1, < 0 0 reconstitution.m experimental phase space reconstruction function lorenz_phase.m function lorenz system test with CC method lorenz_sim.mdl established lorenz system with simulink)

控制规律的选用要根据过程特性和工艺要求来选取，决不是说PID控制规律在任何情况下都具有较好的控制性能，不分场合都采用是不明智的。如果这样做，只会给其它工作增加复杂性，并给参数整定带来困难。当采用PID控制器还达不到工艺要求，则需要考虑其它的控制方案。如串级控制、前馈控制、大滞后控制等。 (Choice of control law according to process characteristics and process requirements to select, does not mean that PID control law in any case have a better control performance, are used regardless of where it would be unwise. If you do, will only add to the complexity of other work, and to the parameter tuning difficult. When using the PID controller has not yet reached technological requirements, you need to consider other control options. Such as cascade control, feedforward control, large time delay control.)

ofdm simulation using BPSK & its BER plot

无模型自适应（MFA）控制 不需要精确模型，通过自适应算法控制。(MFA control)

code for discrete cosine transform

经济学的数值方法 非线性优化。涉及Broyden s Method Powell s Hybrid Method,定价问题(Broyden s Method Powell s Hybrid Method，pricing set)

here is simulation of wirless sensors network and arbre building root node , it s simple code to understand , check it

隐马尔科夫模型是关于时序的概率模型，描述由一个隐藏的马尔科夫链随机生成不可观测的状态随机序列，再由各个状态生成一个观测而产生观测序列的过程。隐藏的马尔科夫链随机生成的状态的序列，称为状态序列；每个状态生成一个观测，而由此产生的观测的随机序列，称为观测序列。马尔科夫链由初始概率分布、状态转移概率分布以及观测概率分布确定(The hidden Markov model is a probabilistic model for time series. It describes the process of randomly generating unobservable state random sequences from a hidden Markov chain, and then generating an observation by each state to produce an observation sequence. A sequence of randomly generated states of hidden Markov chains, called a sequence of states; each state produces an observation, and the resulting random sequence of observations is called an observation sequence. Markov chain is determined by initial probability distribution, state transition probability distribution and observation probability distribution)

说明：  Algorithm for Sensor Selection via Convex Optimization