Jacobi-and-Gauss-Seidel

说明：  用matlab编写的采用遗传算法进行图像分割的一个程序(using Matlab prepared using genetic algorithms for image segmentation of a program)

利用matlab中自带的ＰＬＳ模块进行ＰＬＳ建模的程序代码，程序简单明了(Matlab comes PLS module PLS modeling program code, the program is simple)

说明：  SCMA FILES FOR POLAR CODES

简单介绍了intrans的用法，并简单那介绍了研究生写开题报告时应该注意的问题(Intrans introduced the use of simple, and it introduces the students to write a simple opening report should pay attention to the problem)

分别用中心极限定理和Box-Muller方法产生高斯噪声，用于二进制双极性基带通信系统仿真，比较其仿真运行时间及结果。(Central limit theorem and were used Box-Muller method for generating Gaussian noise for binary bipolar baseband communication system simulation, and compared the results of the simulation run time.)

In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.[1] Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.

基于matlab 的ofdm调制，主要采用bpsk的调制方式，有误码率测试(Ofdm matlab-based modulation, mainly bpsk modulation, there is a bit error rate test)

说明：  This code is related to the course Digital Communications. It simulates a communication system which uses AWGN and 16QAM

Differential Evolution with DEoptim

本文采用小波神经网络进行交通流量预测，短时交通流量存在随机性和非线性因素，影响预测的准确性。传统预测模型难以反映交通流量变化特点，同时传统神经网络易陷入局部极小值，泛化能力差，交通流量预测精度低。为了提高短时交通流量预测精度，提出一种小波神经网络的短时交通流量预测模型。小波神经网络可以对短时交通流量随机性、不确定性进行局部分析，并进行非线性预测，验证了模型的有效性，进行了对比试验。验证结果表明，小波神经网络提高了短时交通流量预精度，预测结果更具应用价值。(In this paper, wavelet neural network is used to forecast traffic flow. There are random and nonlinear factors in short-term traffic flow, affecting the accuracy of prediction. The traditional prediction model is difficult to reflect the characteristics of traffic flow changes. At the same time, the traditional neural network is easy to fall into the local minimum, the generalization ability is poor, and the prediction accuracy of traffic flow is low. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow prediction model based on wavelet neural network is proposed. The wavelet neural network can analyze the randomness and uncertainty of short-term traffic flow and perform nonlinear prediction. The validity of the model is verified and a comparative experiment is conducted. The verification results show that the wavelet neural network improves the short-term traffic flow pre-precision, and the prediction result has more application value.)