The singular value decomposition (SVD) is not only a classical theory in matrix computation and analysis, but also is a powerful tool in ma- chine learning and modern data analysis. In this tutorial we first study the basic notion of SVD and then sh
FEATURES High accuracy
0.02% maximum nonlinearity,0 V to 2 Vrms input
0.10% additional error to crest factor of3
Wide bandwidth
8 MHz at 2Vrms input600 kHz at 100 mVrms ComputesTrue rms Square Mean square Absolute value dB output (60 dB range)
C
#运用python实现差分进化算法计算函数最大值
import random
import math
import numpy as np
import random
cr = 0.6
Population = np.random.rand(100,2)
cycle = 500
hig , low = math.pi , 0
def eval(x):
y = 2*math.sin(x[0])+math.cos(x[1])
return y
def main():
f
A design using output from a bifrequency, colinear dye laser pumped by short-pulse xenon flash lamp is presented. The stimulated hyper-Raman radiation of near and middle IB may be generated by simultaneous single-double photon resonance enhencement.
