erchengfa

用XWindow绘制曼德博罗集，可以缩放显示，参数调整简单，附有详细说明文档。(Draw Mandelbrot Set with X-Window on Linux, can be printed with different scale, parameters can be modified with ease. Readme document attached.)

MPC模型预测控制算法应用在风电场功率波动平抑的典型案例，参考意义(The MPC model predictive control algorithm is applied in the typical case of wind farm power fluctuations )

MATLAB的源程序,包括产生随机传输数据data的子程序,eject_seq发射序列子程序,BPSK调制序列产生子程序,产生pn1序列的子程序,利用积分法实现BPSK解调,均衡pn1序列的子程序.(MATLAB source, including random data transmission of data subroutine, eject_seq launch sequence subroutine, BPSK modulation sequence generated subroutine, have Promoters sequence subroutine, use integral method to achieve BPSK demodulation, balanced Promoters sequence subroutine.)

说明：  一种基于混合策略的改进粒子群优化算法，包括执行程序hpso.m和参数设定hPSOoptions.m两个源文件。代码清晰，易于改进实现。结果显示有效的提高了SPSO的性能(A hybrid strategy based on improved particle swarm optimization algorithm, including the implementation of procedures and parameters set hpso.m two hPSOoptions.m source file. Code clear and easy to improve the achievement. The results showed that the improvement of effective performance of the SPSO)

PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model imposing stability restrictions using Gibbs sampling y = b0 + y(t-1) b1 + y(t-2) b2 +,...,y(t-k) bk + E, E = N(0,sige*V), sige = gamma(nu,d0), b = N(c,T), V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)( PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model imposing stability restrictions using Gibbs sampling y = b0+ y(t-1) b1+ y(t-2) b2+,...,y(t-k) bk+ E, E = N(0,sige*V), sige = gamma(nu,d0), b = N(c,T), V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k))

use binomial model to price option price by matlab

基于matlab的商场的季节性线性回归销售图源代码(Based on matlab mall seasonal sales source code)

说明：  用Matlab软件计算不同轴承高度下，轴系的截面转角及其变形量(the calculation of shaftings' deflection)

MATLAB 曲线拟合小工具，适合初学者，高手直接无视之( MATLAB M file for curve fitting )

B.Aazhang写的一篇论文，第一次提出了神经网络如何在应用多用户检测中。(B. Aazhang write a thesis, the first time how the neural networks in the application of multi-user detection.)