耀变体是一种非常活跃的活动星系核,研究它的有效谱指数是认识其内部结构和辐射机制的有效方法。文中数据来源于目前已公布的SMARTS(Small and Medium Aperture Research Telescope System)数据库,共682组具有B,V,R,J和K波段的准同时性...耀变体是一种非常活跃的活动星系核,研究它的有效谱指数是认识其内部结构和辐射机制的有效方法。文中数据来源于目前已公布的SMARTS(Small and Medium Aperture Research Telescope System)数据库,共682组具有B,V,R,J和K波段的准同时性的观测数据,用LSP(LombScargle Periodogram)方法研究了其有效谱指数的特性,研究结果表明,3C 454.3光学和红外波段光变之间呈正相关;光学和红外波段光变存在两个主导周期,分别约为1.2年和4.5年;双黑洞结构模型中双黑洞质量比约为2∶1。展开更多
A dynamic hysteresis model based on neural networks is proposed for piezoelectric actuator.Neural network has been widely applied to pattern recognition and system identification.However,it is unable to directly model...A dynamic hysteresis model based on neural networks is proposed for piezoelectric actuator.Neural network has been widely applied to pattern recognition and system identification.However,it is unable to directly model the systems with multi-valued mapping such as hysteresis.In order to handle this problem,a novel hysteretic operator is proposed to extract the dynamic property of the hysteresis.Moreover,it can construct an expanded input space to transform the multi-valued mapping of hysteresis into one-to-one mapping.Then neural networks can directly be used to approximate the behavior of dynamic hysteresis.Finally,the experimental results are presented to illustrate the potential of the proposed modeling method.展开更多
文摘耀变体是一种非常活跃的活动星系核,研究它的有效谱指数是认识其内部结构和辐射机制的有效方法。文中数据来源于目前已公布的SMARTS(Small and Medium Aperture Research Telescope System)数据库,共682组具有B,V,R,J和K波段的准同时性的观测数据,用LSP(LombScargle Periodogram)方法研究了其有效谱指数的特性,研究结果表明,3C 454.3光学和红外波段光变之间呈正相关;光学和红外波段光变存在两个主导周期,分别约为1.2年和4.5年;双黑洞结构模型中双黑洞质量比约为2∶1。
基金supported by the National Natural Science Foundation of China(No.61273184)the Program for Changjiang Scholars and Innovative Research Team in University(No.IRT13097)the Natural Science Foundation of Zhejiang Province(Nos.LY15F030022, LY13E050025,LZ15F030005)
文摘A dynamic hysteresis model based on neural networks is proposed for piezoelectric actuator.Neural network has been widely applied to pattern recognition and system identification.However,it is unable to directly model the systems with multi-valued mapping such as hysteresis.In order to handle this problem,a novel hysteretic operator is proposed to extract the dynamic property of the hysteresis.Moreover,it can construct an expanded input space to transform the multi-valued mapping of hysteresis into one-to-one mapping.Then neural networks can directly be used to approximate the behavior of dynamic hysteresis.Finally,the experimental results are presented to illustrate the potential of the proposed modeling method.
