ofdm(1)

无约束连续函数优化的人工蚁群算法通用MATLAB源码 此源码是对人工蚁群算法的一种实现，用于无约束连续函数的优化求解，对于含有约束的情况，可以先使用罚函数等方法，把问题处理成无约束的模型，再使用本源码进行求解 (Unconstrained optimization of continuous function of artificial ant colony algorithm for general-purpose MATLAB source code。 This source code is an implementation of artificial ant colony algorithm for unconstrained optimization of continuous function to solve for the case containing the constraints, you can first use the penalty function and other methods to deal with the problem into the unconstrained model, then use the origin codes solving)

利用牛顿-拉夫森算法求解分段非线性电路系统的仿真程序。(Using Newton- Raphson algorithm for solving non-linear sub-circuit system simulation program.)

说明：  计算数组平均值，以求平均电流，进行异步电机驱动系统逆变器故障检测(Calculate the average value of the array to get the average current and detect the fault of the inverters in the asynchronous motor drive system)

单次脉冲产生,或者周期脉冲产生.周期时间或脉冲宽度可以调节.(Single pulse, or cycle pulse. Cycle time or pulse width can be adjusted.)

MUSIC algorithm for spectrum estimation

基本粒子群算法源代码及解析，解析非常详细(The particle swarm algorithm source code and resolve a very detailed, analytical)

一个实现Adaboost训练和分类的matlab程序，带数据(Adaboost training and classification procedures, with data)

二进制粒子群算法在matlab中应用，程序简单(Application binary PSO algorithm in matlab simple program)

机器人各种机构的控制仿真例程，代码都能运行，适合初学者(Robot control simulation routines of various institutions, the code can run, suitable for beginners)

说明：  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)