GPSR_6.0

一种新的符号定时和载波频率同步算法,该算法将OFDM符号分成几部分。它使用相关性和积累在几个OFDM 符号获得估计函数。当部分的长度小于ISI自由地区,该算法可以识别它。然后估计的最优符号定时和载波频率偏移可以获得。计算机仿真结果表明,该算法不仅可以跟踪时变信道变化但也有较小的MSE比ML估计算法。(A new symbol timing and carrier frequency synchronization algorithm is presented and the algorithm divides the OFDM symbol into several parts. It uses correlation and accumulation in several OFDM symbols to acquire estimation function. When the length of part is smaller than that of ISI free region , the algorithm can distinguish it. Then the estimation of optimal symbol timing and carrier frequency offset can be acquired .The computer simulation results show that the proposed algorithm not only can track time varying channel change but also has smaller MSE than that of ML estimation algorithm.)

用MATLAB实现自适应神经模糊推理系统（ANFIS）结构训练。代码中，首先创建一个初始原ANFIS结构，然后采用遗传算法（GA）、粒子群优化（PSO）来训练ANFIS。此进化训练算法可用于解决非线性回归函数逼近问题。(Implementation of adaptive neural fuzzy inference system (ANFIS) based on MATLAB. Code, the first to create an initial original ANFIS structure, and then using the genetic algorithm (GA), particle swarm optimization (PSO) to train ANFIS. This evolutionary training algorithm can be used to solve the nonlinear regression function approximation problem.)

选取试探函数求解双脊波导中截止频率和场分布(Selecting trial function to solve cut-off frequency and field distribution in double ridge waveguide)

jisuanlm 统计量 y用于空间计量的分析与研究(caculate statistics of lm)

拉格朗日（Lagrange）多项式插值算法源代码这是一个Matlab实现的拉格朗日多项式插值算法，它使用拉格朗日多项式插值的方法近似点定义函数。(Lagrange (Lagrange) polynomial interpolation algorithm source code This is a Matlab realization of Lagrange polynomial interpolation algorithm, which uses the Lagrange polynomial interpolation method similar to the definition of function points.)

说明：  使用matlab对通信系统中的高斯白噪声信道进行建模(Communication systems using matlab on Gaussian white noise channel model)

economic load dispatch power system

Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper presents and proves in detail a convergence theorem for Q,-learning based on that outlined in Watkins (1989). We show that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed each iteration, rather than just one.

MATLAB学习，PPT文件。完全深入简出的教学方法，从最简单的着手。(MATLAB Learning, PPT files. Completely in-depth Jane out of teaching methods, from the simplest place to start.)

维纳滤波器的函数调用，实现界面化操作，函数实现了简单的模糊与解模糊操作(Wiener filter function call, the realization of the interface of the operation)