LTM1

newton criterion simulation matlab

Matlab6应用开发指南,介绍matlab6的应用与开发，数值计算与统计，M文件编辑等(Matlab6 Application Development Guide, Introduction matlab6 the application and development of numerical and statistical, M file editing, etc.)

非均匀阵列 + MUSIC 算法的 DOA 例程(DOA routine of non-uniform array + MUSIC algorithm)

： 讨论了一种采用 D S P芯片T MS 3 2 0 L F 2 4 0 7实现无传感器永磁同步电机转子磁场定向矢量控制的方案， 介 绍了永磁同步电机的数学模型和矢量控制的基本原理， 给出了在无位置传感器的情况下电机转子位置信号的确定 方法， 采用i ， = 0的对凸极式转子磁路结构的无传感器永磁同步电动机的控制方法， 说明采用高速数字信号处理器 易于实现复杂的矢量控制算法， 可以有效地解决电机的强耦合特性．(： A s o l u t i o n t o c o n t r o l a p e r ma n e n t ma g n e t s y n c h r o n o u s m o t o r ( P MS M)o f s e n s o r l e s s u s i n g t h e TM$ 3 2 0LF 2 40 7 i S d i s c u s s e d． A br i e f i n t r o d u c t i o n o f t h e ma t h e ma t i c mo d e l a n d t he v e c t o r c o n t r o l o f PMS M i s p r e s e n t e d，a n d t h e me t h o d o f a s c e r t a i n i n g t h e r o t o r p o s i t i o n u nd e r t h e c i r c u ms t a n c e o f s p e e d s e n s o r l e s s i s a l s o me n t i o n e d．Th e c o n t r o l me t h o d o f i d 0 o n s a l i e n t p o l e PMSM wh i c h i s s e n s or l e s s i s p ut f o r wa r d，wh i c h s h o ws t h a t i f we us e t h e d i g i t a l s i g n a l p r o c e s s o r we c a n ma k e t h e c o mpl i c a t e d v e c t o r c o n— t r o l a r i t h me t i c c o me t r u e e a s i l y，a n)

关于一个轴承故障分解程序，里面包含小波去噪，重构等，还有EMD模态分解，并且还有各种功率谱仿真图(About a bearing failure decomposition process, which contains the wavelet de-noising, reconstruction, etc., as well as mode decomposition EMD, and there are a variety of power spectrum simulation map)

遗传算法优化BP神经网络-非线性函数拟合(BP neural network optimized by genetic algorithm for nonlinear function fitting)

是matlab写的求解哈夫曼编码的程序。(is written in Matlab for Huffman coding procedures.)

relevant vector machine

用基带等效的方式仿真16-QAM载波调制信号在AWGN信道下的误码率和误比特率，并理论相比较(16-QAM carrier modulation signal the AWGN channel bit error rate and bit error rate equivalent baseband simulation and theoretical comparison)

matlab的神经网络，可以用来预测，分类(matlab neural network can be used to predict classification)