ear-matlab

31个城市的VRP进行蚁群算法求解的matlab源码(31 cities, ant colony algorithm for VRP to the matlab source code)

自适应噪声控制程序Matlab编写，LMS算法(Aplication Program to Test Adaptive Noise (Interference) Canceller)

（仅供学习使用）本文重点 研究了 Blueto oth 无线传输协议的 跳频 算法 ,给出 了 算法流程及分析说明 ,并且对信道跳频序 列 进行了 初步 的 性能 分析。 结果表明 该序列 具有良 好的 均 匀 性和随机性((for learning) this paper focuses on the Blueto oth wireless transmission protocol Algorithm, gives the flow and analysis of algorithm of channel frequency hopping sequence, and conducted a preliminary analysis of the performance. The results show that the sequence has a good average The uniform and random)

中值滤波去噪 均值滤波去噪 标准去噪模型 效果较好！(Median filtering noise filtering noise standards mean better denoising model!)

Image enhancement code using matlab algorithm. Very good performance.

Principal scheme of the MIMO toolbox is shown in Fig. 1. Desired pattern taken a discrete alphabet is used to generate testing data. The data are then encoded in the channel encoder and split into the multiple parallel streams. T

用Runge-Kutta 4阶算法对初值问题按不同步长进行求解，用于观察稳定区间的作用。(With a four order Runge- Kutta algorithm for initial value problems in asynchronous long, used to observe stability range.)

属于马尔科夫蒙特卡洛仿真的一种快速收敛方法gibbs抽样(A fast convergence method gibbs sampling belonging to Markov Monte Carlo simulation)

说明：  遗传算法，MATLAB例程，交叉适应繁殖变异，结构清晰(Genetic algorithm, MATLAB routine, cross-breeding to adapt to variations in the structure of clear)

包括：用基本粒子群算法求解无约束优化问题，用带压缩因子的粒子群算法求解无约束优化问题，用线性递减权重粒子群优化算法求解无约束优化问题，用自适应权重粒子群优化算法求解无约束优化问题，用随机权重粒子群优化算法求解无约束优化问题，用学习因子同步变化的粒子群优化算法求解无约束优化问题，用学习因子异步变化的粒子群优化算法求解无约束优化问题，用二阶粒子群优化算法求解无约束优化问题，用二阶振荡粒子群优化算法求解无约束优化问题(Include: using the particle swarm algorithm for unconstrained optimization problems, with compression factor of particle swarm algorithm for solving unconstrained optimization problem with linear decreasing weight particle swarm optimization for unconstrained optimization problems, an adaptive weight particle swarm optimization unconstrained optimization problems, random weight particle swarm optimization algorithm for unconstrained optimization problems, with the learning factor synchronous change of particle swarm optimization algorithm for unconstrained optimization problems, with the learning factor asynchronous changes in particle swarm optimization algorithm for solving unconstrained optimization problem , with the second particle swarm optimization for unconstrained optimization problems, with the second-order oscillating particle swarm optimization algorithm for unconstrained optimization problem)