Series resonant ZCS‐PFM DC‐DC power converter with high‐frequency high‐voltage transformer link for high‐power magnetron drive

This paper presents a single lossless inductive snubber‐assisted ZCS‐PFM series resonant DC‐DC power converter with a high‐frequency high‐voltage transformer link for industrial‐use high‐power magnetron drive. The current flowing through the active power switches rises gradually at a turned‐on transient state with the aid of a single lossless snubber inductor, and ZCS turn‐on commutation based on overlapping current can be achieved via the wide range pulse frequency modulation control scheme. The high‐frequency high‐voltage transformer primary side resonant current always becomes continuous operation mode, by electromagnetic loose coupling design of the high‐frequency high‐voltage transformer and the magnetizing inductance of the high‐frequency high‐voltage transformer. As a result, this high‐voltage power converter circuit for the magnetron can achieve a complete zero current soft switching under the condition of broad width gate voltage signals. Furthermore, this high‐voltage DC‐DC power converter circuit can regulate the output power from zero to full over audible frequency range via the two resonant frequency circuit design. Its operating performances are evaluated and discussed on the basis of the power loss analysis simulation and the experimental results from a practical point of view. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 153(3): 79–87, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20126     

重积分计算方法的一个注记

三重积分与三次积分的意义是不相同的。但是在一定条件下三重积分可以转化为三次累次积分来计算,其公式为:■     

Time series analysis of wind speed using VAR and the generalized impulse response technique

This research examines the interdependence in time series wind speed data measured in the same location at four different heights. A multiple-equation system known as a vector autoregression is proposed for characterizing the time series dynamics of wind. Additionally, the recently developed method of generalized impulse response analysis provides insight into the cross-effects of the wind series and their responses to shocks. Findings are based on analysis of contemporaneous wind speed time histories taken at 13, 33, 70 and 160 ft above ground level with a sampling rate of 10 Hz. The results indicate that wind speeds measured at 70 ft was the most variable. Further, the turbulence persisted longer at the 70-ft measurement than at the other heights. The greatest interdependence is observed at 13 ft. Gusts at 160 ft led to the greatest persistence to an “own” shock and led to greatest persistence in the responses of the other wind series.     

关于移动平均过程两系数的推导

对经济预测方法中两公式给出了证明，两公式的系数被显式推导出来，这两个公式是时间序列模型公式，广泛用于经济预测之中，但文献上很难找到推导过程，本文的证明是基本的，无需高深数学知识即可理解．     

Identification environment and robust forecasting for nonlinear time series

In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance.     

非线性动态系统“黑箱”建模方法的研究

本文研究弱非线性动态系统“黑箱”建模的理论和方法，并简要给出根据此方法建模的结果。     

THE RECURSIVE FITTING OF SUBSET VARX MODELS

Abstract. A vector time series model of the form A(L)y(t) + B(L)x(t) =ε(t) is known as a vector autoregressive model with exogenous variables (VARX model) and involves a regressand vector y(t) and a regressor vector x(t). This paper provides a method for the recursive fitting of subset VARX models. It suggests the use of ascending recursions in conjunction with an order selection criterion to choose an 'optimum' subset VARX model.     

松材线虫病害高光谱时序与敏感特征研究

采用高光谱仪ASD FieldSpec Pro FR,连续观测了健康和发病马尾松针叶的时序高光谱,分析了松材线虫病害光谱的时序特征、敏感时期和敏感特征。结果表明:与健康马尾松相比,病害马尾松时序光谱差异较大;病害首先造成红边区域内光谱反射率减低,然后再出现红边蓝移的2阶段光谱变化规律;感染松材线虫的马尾松9月初已经出现了病态植被典型的光谱特征;近红外平台内最大的一阶微分值、红边内一阶微分的总和(SDr)与蓝边内一阶微分的总和(SDb)的比值等是指示病害发生的显著性高光谱特征。     

Hybrid function projective synchronization of chaotic systems with uncertain time-varying parameters via Fourier series expansion

In this paper, the hybrid function projective synchronization (HFPS) of different chaotic systems with uncertain periodically time-varying parameters is carried out by Fourier series expansion and adaptive bounding technique. Fourier series expansion is used to deal with uncertain periodically time-varying parameters. Adaptive bounding technique is used to compensate the bound of truncation errors. Using the Lyapunov stability theory, an adaptive control law and six parameter updating laws are constructed to make the states of two different chaotic systems asymptotically synchronized. The control strategy does not need to know the parameters thoroughly if the time-varying parameters are periodical functions. Finally, in order to verify the effectiveness of the proposed scheme, the HFPS between Lorenz system and Chen system is completed successfully by using this scheme.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.