duandianjiance

Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga, B. Basturk Akay, C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, LNCS: Modeling Decisions for Artificial Intelligence, 4617/2007, 318-329, 2007. D. Karaboga, C. Ozturk, Neural Networks Training by Artificial Bee Colony Algorithm on Pattern Classification, Neural Network World, 19(3), 279-292, 2009. */

潮流准调和分析 Dd.m [DO1 dO1 DK1 dK1 DM2 dM2 DS2 dS2 DM4 dM4 DMS4 dMS4]=Dd(y,D,Y,tm,A1) 计算6个准调和分潮的振幅系数和初相角公式 aM.m [N n aa liu_u liu_v]=aM() 基础系数矩阵，目前D和d值是取用的各段数据的中间时间值，t是以中间为0左右对称取； 视差潮龄A1需要预先得知 同时本程序负责读取原始测流数据，格式为 站位名称 观测数据段数N（即大 中 小潮） 第一段起始时间YYYY MM DD HH mm ss 第一段个数n1 speed direction speed direction ... 第二段起始时间 YYYY MM DD HH mm ss 第二段个数n2 speed direction speed direction ... 第三段起始时间 YYYY MM DD HH mm ss 第三段个数n3 speed direction speed direction ... UVg.m [U V Ug Vg]=UVg() 求调和常数U V和迟角Ug Vg Tuoyuan.m 计算各准调和分潮的椭圆要素W theta K k (The quasi harmonic analysis of tidal current Dd.m [DO1 dO1 DK1 dK1 DM2 dM2 DS2 dS2 DM4 dM4 DMS4 dMS4]=Dd (y, D, Y, TM, A1) calculation of 6 quasi harmonic division of amplitude and initial phase angle formula of the coefficient of tide AM.m [N n AA liu_u liu_v]=aM () foundation coefficient matrix, the current D and D values are the middle time of each section of data taken the value of T is in the middle for symmetric about 0 Age of parallax A1 need to know in advance At the same time, this program is responsible for reading the original flow measurement data, format for Site name The observation data segment number N (that is, in the low tide) The first section of the YYYY MM DD HH mm the starting time of SS The first section number N1 Speed direction Speed direction ... The second section starting time YYYY MM DD HH mm SS The second section number N2 Speed direction Speed direction ... The third section starting time YYYY MM DD HH mm SS The third section number )

利用matlab进行时域分析，本实验首录了三个音频。并编写程序求其特征值。特征值有平均值，整流平均值方差，标准差，方差，峭度，均方根，波形因子，峭度因子，峰值因子，脉冲因子，裕度因子。还有就是对音频进行自相关分析得出其波形图。(Use matlab time-domain analysis, the first experiment recorded three audio. Programming and seeking the eigenvalues. Characteristic values are averaged rectified mean variance, standard deviation, variance, kurtosis, RMS, crest factor, kurtosis factor, crest factor, pulse factor, margin factor. There is an audio self-correlation analysis of its waveform.)

说明：  simulink建的模型,用于直线电机,仿真时数据要自己加入(simulink model built for the linear motor)

SCWCD Question Papers

关于特性检测的循环相关算法，基于am加噪声的情形(About characteristics of the cyclic correlation detection algorithm, based on the circumstances am add noise)

Channel Model Generation helpful(Channel Model Generation very helpful)

利用matlab计算lyapunov最大指数，对混沌时间序列进行短期预测。(Using matlab to calculate lyapunov largest index of short-term prediction of chaotic time series.)

Prony分析是获取系统振荡模式特征的一种非常有效的方法，它可以通过给定输入信号下的响应直接估计系统的振荡频率、衰减、幅值和初相位。本文基于Prony算法提出一种用振荡模式能量级鉴别电力系统大干扰下主导低频振荡模式的方法。(Prony analysis acquisition system oscillation mode features a very effective way, It can set the input signal to the estimated direct response to the oscillation frequency, attenuation, amplitude and phase. Based on Prony algorithm proposed by the energy level oscillation mode differential interference large power systems under low-frequency oscillation mode lead style approach.)

converts cell array into scalar float array (v3.0, dec 2009)