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2015年重点实验室发表的代表性论文

发布时间 :2015-12-31 来源:   已浏览:

[1]      Zhao S Y, Shmaliy Y S, Huang B, Liu F. Minimum variance unbiased FIR filter for discrete time-variant systems. Automatica, 2015, 53: 355-361.

[2]      Yin Y Y, Shi P, Liu F, Teo K L and Lim C C, Robust filtering for nonlinear nonhomogeneous Markov jump systems by fuzzy approximation approach. IEEE Transactions on Cybernetics, 2015, 45(9): 1706-1716.

[3]      Chen L, Khatibisepehr S, Huang B, Liu F. Yongsheng Ding. Nonlinear process identification in the presence of multiple correlated hidden scheduling variables with missing data. AIChE Journal, 2015, 61(10): 3270-3287.

[4]      Zhao S Y, Huang B, Liu F. Fault detection and diagnosis of multiple-model systems with mismodeled transition probabilities. IEEE Transactions on Industrial Electronics, 2015, 62(8): 5063-5071.

[5]      Xie L B, Shieh L S, Wu C Y, Tsai J S H, Canelond J ISingla M. Digital sliding mode controller design for multiple time-delay continuous-time transfer function matrices with a long input-output delay. Journal of Process Control, 2015, 25: 78-83.

[6]      Zhao S Y, Shmaliy Y S, Liu F.Fast computation of discrete optimal FIR estimates in white Gaussian noise. IEEE Signal Processing Letters, 2015, 22(6): 718-722.

[7]      Xu D Z, Jiang B, Shi P. Robust NSV fault-tolerant control System design against actuator faults and control surface damage under actuator dynamics. IEEE Transactions on Industrial Electronics, 2015, 62(9): 5919-5928.

[8]      Zhao Z G, Li Q H, Huang B, Liu F, Ge Z Q. Process monitoring based on factor analysis: Probabilistic analysis of monitoring statistics in presence of both complete and  incomplete measurements. Chemometrics and Intelligent Laboratory Systems, 2015, 142: 18-27.

[9]      Yanyan Yin, Peng Shi, Fei Liu, and Kok Lay Teo, A novel approach to fault detection for fuzzy stochastic systems with nonhomogeneous processes, Information Science, 2015, 292(2): 198-213.

[10]  Zhao S Y, Shmaliy Y S, Liu F. Effect of embedded unbiasedness on discrete-time optimal FIR filtering estimates. EURASIP Journal on Advances in Signal Processing, 2015, 2015: 1-13.

[11]  Ding F, Wang Y J, Ding J. Recursive least squares parameter identification for systems with colored noise using the filtering technique and the auxiliary model. Digital Signal Processing, 2015, 37: 100-108.

[12]  Luan X L, Min Y, Ding Z T, Liu F, Stochastic finite-time consensualisation for Markov jump networks with disturbance, IET Control Theory & Applications, 2015, 9(16): 2340-2347.

[13]  Ding F, Meng D D, Wang Q. The model equivalence based parameter estimation methods for Box-Jenkins systems. Journal of the Franklin Institute, 2015, 352(12): 5473­5485.

[14]  Chen H B, Ding F. Hierarchical least squares identification for Hammerstein nonlinear controlled autoregressive systems. Circuits, Systems and Signal Processing, 2015, 34(1): 61-75.

[15]  Chen H B, Ding F, Xiao Y S. Decomposition-based least squares parameter estimation algorithm for input nonlinear systems using the key term separation technique. Nonlinear Dynamics, 2015, 79(3): 2027-2035.

[16]  Chen F Y, Ding F, Li J H. Maximum likelihood gradient-based iterative estimation algorithm for a class of input nonlinear controlled autoregressive ARMA systems. Nonlinear Dynamics, 2015, 79(2): 927-936.

[17]  Chen F Y, Ding F, Sheng J. Maximum likelihood based recursive parameter estimation for controlled autoregressive ARMA systems using the data filtering technique. Journal of the Franklin Institute, 2015, 352(12): 5882-5896.

[18]  Gu Y, Ding F, Li J H. States based iterative parameter estimation for a state space model with multi-state delays using decomposition. Signal Processing, 2015, 106: 294-300.

[19]  Guo L J, Ding F. Least squares based iterative algorithm for pseudo-linear autoregressive moving average systems using the data filtering technique. Journal of the Franklin Institute, 2015, 352(10): 4339-4353.

[20]  Ma X Y, Ding F. Recursive and iterative least squares parameter estimation algorithms for observability canonical state space systems. Journal of the Franklin Institute -- Engineering and Applied Mathematics, 2015, 352(1): 248-258.

[21]  Ma X Y, Ding F. Gradient-based parameter identification algorithms for observer canonical state space systems using state estimates. Circuits, Systems and Signal Processing, 2015, 34(5): 1697-1709.

[22]  Mao Y W, Ding F. Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique. Nonlinear Dynamics, 2015, 79(3): 1745-1755.

[23]  Shen Q Y, Ding F. Iterative identification methods for input nonlinear multivariable systems using the key-term separation principle. Journal of the Franklin Institute, 2015, 352(7): 2847-2865.

[24]  Wang X H, Ding F. Recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle. Signal Processing, 2015, 117: 208-218.

[25]  Wang X H, Ding F. Convergence of the auxiliary model based multi-innovation generalized extended stochastic gradient algorithm for Box-Jenkins systems. Nonlinear Dynamics, 2015, 82(1-2): 269-280.

[26]  Liu C L, Liu F. Collective behavior of mixed-order linear multi-agent systems under output-coupled consensus algorithm. Journal of The Franklin Institute, 2015, 352(9): 3585-3599.

[27]  Liu C Lin, Liu F. Delayed-compensation algorithm for second-order leader-following consensus seeking under communication delay. Entropy, 2015, 17(6): 3752-3765.

[28]  Luan X L, Chen Q, Liu F, Equivalent Transfer Function based Multi-loop PI Control for High Dimensional Multivariable Systems. International Journal of Control, Automation, and Systems, 2015, 13(2): 1-7.

[29]  Luan X L, Zhao C Z, Liu F, Finite-time stabilization of switching Markov jump systems with uncertain transition rates. Circuits, Systems & Signal Processing, 2015, 34(12): 3741-3756.

[30]  Lou X Y, Cui B T. Adaptive consensus filters for second-order distributed parameter systems using sensor networks. Circuits, Systems & Signal Processing, 2015, 34(9): 2801-2818.

[31]  Jiang Z X, Cui B T. Estimation of spatially distributed processes using mobile sensor networks with missing measurements. Chinese Physics B, 2015, 24(2): 020702.

[32]  Zhu Q, He C, Lu R, Mendoza F, Cen H. Ripeness evaluation of ‘Sun Bright’ tomato using optical absorption and scattering properties. Postharvest Biology and Technology, 2015, 103: 24-34.

[33]  Huang M, Zhao W, Wang Q, Zhang M, Zhu Q. Prediction of moisture content uniformity using hyperspectral imaging technology during the drying of maize kernel. International Agrophysics, 2015, 29: 39-46.

[34]  Huang M, Wang Q, Zhu Q, Qin J, Huang G. Review of quality and safety tests using optical sensing technologies. Seed Science and Technology, 2015, 43(3): 337-366.

[35]  Xiong W L, Yang X Q, Ke L, Xu B G. EM algorithm-based identification of a class of nonlinear Wiener systems with missing output data. Nonlinear Dynamics, 2015, 80(1-2): 329-339.

[36]  Qin L L, Zheng X, Tian G Y. A trajectory-based Coverage assessment approach for universal sensor networks. Sensor, 2015, 15: 19649-19666.

[37]  Liu D, Zhang H, Xu B, Tan J. Development of a kinetic model structure for simultaneous saccharification and fermentation in rice wine production. Journal of the Institute of Brewing, 2015, 121(4): 589-596.

[38]  Xu L, Chen L, XiongW L. Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration. Nonlinear Dynamics, 2015, 79(3): 2155-2163.

[39]  Yin Y Y, Shi P, Liu F, and Kok Lay Teo, Robust L2-L∞ filtering for a class of dynamical systems with nonhomogeneous Markov jump process, International Journal of Systems Science, 2015, 46(4), 599-608.

[40]  Ding J, Cichy B, Galkowski K, Rogers E, Yang H Z. Robust fault-tolerant iterative learning control for discrete systems via linear repetitive processes theory. International Journal of Automation and Computing, 2015,12(3): 254-265.

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