High Linear Voltage Gain in QZNC Through Synchronizing Switching Circuits

The solar powered systems require high step-up converter for efficient energy transfer.For this,quasi-impedance network converter has been introduced.The quasi-impedance network converter(QZNC)is of two types:type-1 and type-2 configuration.Both the type-1 and type-2 QZNC configurations have drooping voltage gain profile due to presence of high switching noise.To overcome this,a new quasi-impedance network converter synchronizing the switching circuit with low frequency noise has been proposed.In this paper,the proposed QZNC con-figuration utilizes the current controlling diode to prevent the output voltage drop.Thus,the suggested topology provides linear high voltage gain profile,low load voltage ripple,and reduced impedance network stress and device stress.There-fore,the efficiency of proposed QZNC has been improved.The topology descrip-tion,working principle,parameter design and comparison with traditional converters are illustrated.Andfinally,both simulation and practical results are presented to confirm the converter characteristics and performance.From the results,it has been found that the performance of the suggested topology is better as it achieves a higher efficiency of 81%and hence,it is suitable for high power applications.

Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix

This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet (PST) of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a novel information criterion (NIC), in which the stationary points are singular triplet of the crosscorrelation matrix. Then, based on Newton's method, we obtain a coupled system of ordinary differential equations (ODEs) from the NIC. The ODEs have the same equilibria as the gradient of NIC, however, only the first PST of the system is stable (which is also the desired solution), and all others are (unstable) saddle points. Based on the system, we finally obtain a fast and stable algorithm for PST extraction. The proposed algorithm can solve the speed-stability problem that plagues most noncoupled learning rules. Moreover, the proposed algorithm can also be used to extract multiple PSTs effectively by using sequential method. © 2014 Chinese Association of Automation.