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Air combat target maneuver trajectory prediction based on robust regularized volterra series and adaptive ensemble online transfer learning

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Defence Technology（防务技术） 2023年第2期20卷 187-206页

作者： Xi Zhi-fei Kou Ying-xin Li Zhan-wu Lv Yue Xu An Li You Li Shuang-qing Air Force Engineering University No.1 Baling RoadBaqiao DistrictXi'anShaanxi ProvinceChina

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver ***,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be *** solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and volterra series,abbreviated as AERTrOS-volterra is ***,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory ***,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the Trvolterra algorithm framework,a robust regularized online Sequential volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori ***,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of *** with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current predi

关键词： Maneuver trajectory prediction volterra series Transfer learning Online learning Ensemble learning Robust regularization

Power spectral density analysis for nonlinear systems based on volterra series

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Applied Mathematics and Mechanics(English Edition) 2021年第12期42卷 1743-1758页

作者： Penghui WU Yan ZHAO Xianghong XU State Key Laboratory of Structural Analysis for Industrial Equipment Faculty of Vehicle Engineering and MechanicsDalian University of TechnologyDalian 116023Liaoning ProvinceChina State Key Laboratory of Nonlinear Mechanics Institute of MechanicsChinese Academy of SciencesBeijing 100190China

A consequence of nonlinearities is a multi-harmonic response via a monoharmonic excitation.A similar phenomenon also exists in random *** power spectral density(PSD)analysis of random vibration for nonlinear systems is studied in this *** analytical formulation of output PSD subject to the zero-mean Gaussian random load is deduced by using the volterra series expansion and the conception of generalized frequency response function(GFRF).For a class of nonlinear systems,the growing exponential method is used to determine the first 3 rd-order *** proposed approach is used to achieve the nonlinear system’s output PSD under a narrow-band stationary random *** relationship between the peak of PSD and the parameters of the nonlinear system is *** using the proposed method,the nonlinear characteristics of multi-band output via single-band input can be well *** results reveal that changing nonlinear system parameters gives a one-of-a-kind change of the system’s output *** paper provides a method for the research of random vibration prediction and control in real-world nonlinear systems.

关键词： volterra series nonlinear system generalized frequency response function(GFRF) power spectrum density(PSD)

THE PARALLEL RECURSIVE AP ADAPTIVE ALGORITHM BASED ON volterra series

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Journal of Pharmaceutical Analysis 2005年第2期17卷 3-6页

作者：孔祥玉魏瑞轩韩崇昭 School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an 710049 China

Aiming at the nonlinear system identification problem, a parallel recursive affine projection (AP) adaptive algorithm for the nonlinear system based on volterra series is presented in this paper. The algorithm identifies in parallel the volterra kernel of each order, recursively estimate the inverse of the autocorrelation matrix for the volterra input of each order, and remarkably improve the convergence speed of the identification process compared with the NLMS and conventional AP adaptive algorithm based on volterra series. Simulation results indicate that the proposed method in this paper is efficient.

关键词： nonlinear system volterra series adaptive identification AP algorithm

Detection and classification of multiple power signal patterns with volterra series and interval type-2 fuzzy logic system

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Protection and Control of Modern Power Systems 2017年第1期2卷 92-101页

作者： Rahul Rajiv Kapoor M.M.Tripathi Department of Electronics&communication Engineering Delhi Technological UniversityDelhiIndia Department of Electrical Engineering Delhi Technological UniversityDelhiIndia.

The paper deals with the application of volterra bound Interval type−2 fuzzy logic techniques in power quality *** work proposes a new layout for detection,localization and classification of various types of power quality *** proposed method exploits volterra series for the extraction of relevant features,which are used to recognize different PQ events by Interval type-2 fuzzy logic based *** single as well as multiple powers signal disturbances have been simulated to testify the efficiency of the proposed *** time–frequency analysis results in the clear visual detection,localization,and classification of the different power quality *** simulation results signify that the proposed scheme has a higher recognition rate while classifying single and multiple power quality events unlike other ***,the proposed method is compared with SVM,feed forward neural network and type−1 Fuzzy logic system based classifier to show the efficacy of the proposed technique in classifying the Power quality events.

关键词： Non-stationary power signals Power quality(PQ) volterra series Interval type-2 fuzzy logic system(IT2FLS) Power Spectral Entropy(PSE) Standard Deviation(SD)

Fault Diagnosis Method of Non-linear Analog Circuits Based on volterra series and SVM

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Fault Diagnosis Method of Non-linear Analog Circuits Based o...

Nonlinearity Analysis of CMOS LNA Based on volterra series

第26届中国控制与决策会议

作者： Huafeng He Yicheng Chen Zheng Yang Huixuan Deng The Second Artillery Engineering University

A new fault diagnosis method of analog circuits is proposed in this paper. The method is based on volterra series and SVM. This paper introduces the basic theory of volterra series and SVM and deduces the proposed identification method in detail. The identification method is applied to the fault diagnosis of a non-linear analog circuit. The simulation result shows that the proposed method can obviously improve the fault diagnosis rate compared with the traditional method.

关键词： volterra series SVM Fault Diagnosis

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Nonlinearity Analysis of CMOS LNA Based on Volterra Series

The Ship Motion Prediction Approach Based on BP Neural Network to Identify volterra series Kernels 26th

2002海峡两岸三地无线科技研讨会

作者： ZHANG Wei-cheng, CHEN Kang-sheng Department of Information and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

A volterra series-based approach to nonlinearity analysis of RF CMOS LNA is given. Analytical expressions describing nonlinear behavior of source degenerated and cascode CMOS LNAs are derived and veri

关键词： intermodulation distortion low noise amplifier MOSFET amplifier volterra series

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The Ship Motion Prediction Approach Based on BP Neural Netwo...

A Behavior Level Analytical Method of Electromagnetic Susceptibility for Radio Frequency Receiver

第26届中国控制与决策会议

作者： Xiuyan Peng Zhiguo Men Xingmei Wang Shuli Jia Automation College Harbin Engineering University Economics and Management College Harbin Engineering University Computer Science and Technology College Harbin Engineering University

ISBN: (纸本)9781479937097

Ship motion prediction plays a prominent role in the whole ship motion process. This paper presents a new approach for ship motion prediction. In order to obtain more effective prediction result, the paper studied the BP neural network and volterra series model, and the chaos characteristics of ship motion time series. A novel method of single-output three-layer BP neural network to identify volterra series kernels is proposed. Multi-step prediction for the roll motion time series of ship at 135°with the method is accomplished. The simulation analysis demonstrate that the ship motion prediction approach based on BP neural network to identify volterra series kernels has higher precision, longer prediction time, effectiveness and adaptability, and it can predict the ship motion exactly.

关键词： BP neural network volterra series Ship motion Prediction Multi-step Prediction.

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Chinese Journal of Aeronautics 2011年第1期24卷 74-80页

作者： YAN Zhaowen YU Xiaofeng XIE Shuguo LIU Yan School of Electronics and Information Engineering Beijing University of Aeronautics and Astronautics Beo'ing 100191 China

Based on simplified volterra model, a behavior-level simulation analysis approach of electromagnetic susceptibility （EMS） for radio frequency （RF） receiver is presented in this article. Third order simplified volterra model is adopted to analyze receiver EMS. A general criterion for EMS is proposed according to EMS response characteristics in frequency domain, and then the simulation flow charts for calculating susceptibility thresholds are given. This approach is superior to the previous EMS analysis method on receiver, which mainly relies on experiments but lack simulation analysis. Take the dual frequency GPS receiver and zero IF receiver interfered by continue wave as examples for in-band and out-of-band susceptibility threshold calculation. Simulation results show that the proposed method is not only valid and effective, but also reduces the internal storage and simulation time, which is suitable to RF receiver EMS analysis and practical in engineering and technology field.

关键词： radio frequency receiver electromagnetic susceptibility volterra series sensitivity threshold behavior-level simulation

Reducing force transmissibility in multiple degrees of freedom structures through anti-symmetric nonlinear viscous damping

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Acta Mechanica Sinica 2012年第5期28卷 1436-1448页

作者： Z.-K.Peng Z.-Q.Lang G.Meng S.A.Billings State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Department of Automatic Control and Systems Engineering University of Sheffield

In the present study, the volterra series theory is adopted to theoretically investigate the force transmissibility of multiple degrees of freedom （MDOF） structures, in which an isolator with nonlinear anti-symmetric viscous damping is assembled. The results reveal that the anti-symmetric nonlinear viscous damping can significantly reduce the force trans- missibility over all resonance regions for MDOF structures with little effect on the transmissibility over non-resonant and isolation regions. The results indicate that the vibration isolators with an anti-symmetric damping characteristic have great potential to solve the dilemma occurring in the design of linear viscously damped vibration isolators where an increase of the damping level reduces the force transmissibility over resonant frequencies but increases the transmissibility over non-resonant frequency regions. This work is an extension of a previous study in which MDOF structures installed on the mount through an isolator with cubic nonlinear damping are considered. The theoretical analysis results are also verified by simulation studies.

关键词： Nonlinear viscous damping Force transmissibility - Vibration isolation volterra series