difference-and-fitting-simulation

kmeans算法的描述，源代码实现，matlab上 的程序(kmeans)

matlab，仿真设计，再设计的基础上进行了一定的开发，使得编译连接有了较好的改善(Build connections )

一个简单的adaboost分类的方法。随机生成两组数据，训练分类器。之后再生成两组数据进行分类。效果很好(A simple way adaboost classification. Randomly generated two sets of data, to train a classifier. After re-generate two sets of data classification. Good results)

NSGA2仿真实例，可用于神经网络仿真测试(NSGA is a popular non-domination based genetic algorithm for multi- objective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter. A modified version, NSGA- II was developed, which has a better sorting algorithm , incorporates elitism and no sharing parameter needs to be chosen a priori. NSGA-II is discussed in detail in this.)

tai lieu ve mo phong ofdm

很多神经网络的应用程序：如感知器，自适应线性元件，BP网络，霍普菲尔德网络，竞争网络...适合初学者。(Many neural network applications: such as sensor, adaptive linear element, BP network, Hopfield networks, competitive networks ... suitable for beginners.)

基于ARIMA的交通流预测，预测效果相当不错 (Traffic Flow Prediction Based on ARIMA forecast is quite good)

Anderson 计算流体力学入门第七章的第一个程序(the first program for Anderson Computational Fluid Dynamics )

LMS（数字信号处理--DSP中的自适应滤波算法） 非常详细，可以很清楚的看到LMS的基本原理(LMS (Digital Signal Processing- DSP in the adaptive filtering algorithm) is very detailed, you can clearly see the basic principle of LMS)

随着新的数学工具小波分析的实用化为基于NN负荷预测模型性能的改善提供了理论依据对于电力系统负荷非线性时间序列的辨识在预测方法研究中应给予重视在本文所用的基于小波原理和NN融合的预测原理是具有强的非线性时间序列的辩能力由研究和仿真表明它能有效提高预测的精度(with new mathematical tools wavelet analysis based on NN into practical load forecasting model to improve the performance of the rationale On the basis for the power system load nonlinear time series forecasting methods in literacy research should give importance to the use of paper Based on wavelet theory and NN fusion principle is the forecast of strong nonlinear time series by the dialectical capacity of research and simulation shows it can effectively improve the accuracy of forecasts)