Lecture-note-in-ECCV_2010.pdf

MATLAB遗传算法源码 我是初学 希望能有人指点我一下(MATLAB genetic algorithm source code I am a beginner want to someone show me)

通过matlab实现数据的时域图D代码呵呵呵呵(When implementing data via matlab domain map)

This is the code for vector quantization by using k-means

几个关于阵列天线的仿真程序，运行环境为matlab,需要用到阵列信号处理工具箱，运行极了一看见结果。(Several on the antenna array simulation program, operating environment for matlab, need to resort to array signal processing toolbox, running great to see the results of one.)

LMS另一种写法，亲自验证，用来滤波，自适应滤波的一种(LMS another way, personally verified, used to filter, an adaptive filter)

An effective implicit time integration scheme was proposed for the fi nite element solution of nonlinear problems in structural dynamics. Various important attributes were demonstrated. In particular, it was shown that the scheme remains stable, without the use of adjustable parameters, when the commonly used trapezoidal rule results in unstable solutions. In this paper we focus on additional important attributes of the scheme, and specifi cally on showing that the procedure can also be effective in linear analyses. We give, in comparison to other methods, the spectral radius, period elongation, and amplitude decay of the scheme and study the solution of a simple ‘model problem’ with a very fl exible and stiff response.

code for improved Euler method by using MATLAB program

说明：  解决倒立摆中非线性化问题，使用s-function函数，输入为控制量为u(In order to solve the nonlinear problem of inverted pendulum, the S-function function is used, and the input is u)

基于matlab的小波算法，给出了一个完整的算例，初学者可以很好的学习。(Based on the the matlab wavelet algorithm is given a complete example, beginners can learn well.)

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.)