modoqpsk

基于matlab的脉冲不变法巴特沃思低通滤波器设计(Based on the same method matlab pulse Butterworth low-pass filter design)

Simulink的外文学习资料,适用于英文基础较好的matlab初学者。(err)

code for rssi cALCULATION TO GET IT

Prueba1 FletcherReeves Hernández Santiago José Maestría en Ciencias de la Computación Septiembre / 2011 1. Comenzar con un punto arbitrario 2. Calcular Gradiente de Fi 3. Si el Gradiente Fi es igual a 0(converge), termina 4. Si el Gradiente Fi es !=0 continuar 5. Encontrar dirección de búsqueda Si= -GradienteFi= - Gradiente F(Xi) 6. Determinar la Longitud Optima del incremento lamda(i) en dirección Si X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi) 7. Hacer i=2 8. Obtener Gradiente Fi 9. Calcular Si= -GradFi + ( [abs(GradFi)^2]/[abs(GradF(i-1))^2] )*S(i-1) 10. Determinar la Longitud Optima del incremento lamda(i) en dirección Si X(i+1)=X(i)+lamda(i)*S(i)= X(i)-lamda(i)*Gradiente F(Xi) 7. Verificar Optimalidad de X(i+1) Si es optimo, detener Si no es optimo hacer i=i+1 e ir al paso 8

second sliding mode control of inverted pendulum.

this is a genetic algorithm source code

corridor方差减小技术，是重要的方差减小技术之一。(corridor variance reduction technique)

曲线模型的半自适应交互多模型跟踪方法 我存的一篇论文，拿来与大家共享(When using interactive multiple model (IMM) to track targets,to select smallermodel set to better cover the move- ment property and the maneuvering level of various targets and meanwhile to reduce computation complexity are both worthwhile and also necessary research tasks to project implementation.The main work is done through the IMM algorithm of curvilinear model,the construction of angle velocity estimation model and the extension of it to state equations,whichmake it possible for normal acceleration to be self-adaptively adjusted,hence it reduces the model setto associate onlywithMcurvilinearmodels selatingtotangential acceler- ation.Computation burden is reduced and algorithm’s applying range is extended.The simulation further proves the conclusion )

水印的MATLAB傅立叶算法 有较强的鲁棒性能(watermarking algorithm MATLAB Fourier a strong robust performance)

极限学习机的回归拟合及分类——对比实验研究(Limit the return of learning machine fitting and classification-contrast experiment research)