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On the Realization Theory of Polynomial Matrices and the Algebraic Structure of Pure Generalized State Space Systems

References Bosgra, O. and Van Der Weiden, A. (1981). Realizations in generalized state-space form for polynomial system matrices and the definitions of poles, zeros and decoupling zeros at infinity, International Journal of Control 33(3): 393-411. Christodoulou, M. and Mertzios, B. (1986). Canonical forms for singular systems, Proceedings of the of 25th IEEE Conference on Decision and Control (CDC) , Athens, Greece, pp. 2142-2143. Cobb, D. (1984). Controllability, observability

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Adaptive Prediction of Stock Exchange Indices by State Space Wavelet Networks

References Borowa, A., Brdyś, M.A. and Mazur, K. (2007). Modeling of wastewater treatment plant for monitoring and control purposes by state-space wavelet networks, International Journal of Computers, Communications & Control   II (2): 121-131. Brdyś, M.A., Grochowski, M., Gminski, T., Konarczak, K. and Drewa, M. (2008). Hierarchical predictive control of integrated wastewater treatment systems, Control Engineering Practice   16 (6): 751-767. Grossmann, A. and Morlet, J. (1984

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Disturbance modeling and state estimation for offset-free predictive control with state-space process models

, Springer Verlag, London. Doyle III, F., Ogunnaike, B. and Pearson, R. (1996). Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models, Automatica 32(9): 1285–1301. Gonzalez, A.H., Adam, E.J. and Marchetti, J.L. (2008). Conditions for offset elimination in state space receding horizon controllers: A tutorial analysis, Chemical Engineering and Processing 47(12): 2184–2194. Hesketh, T. (1982). State-space pole-placing self-tuning regulator using input-output values, IEE Proceedings, Part D 129

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Nonlinear State–Space Predictive Control with On–Line Linearisation and State Estimation

-based control using second-order Volterra models, Automatica 31 (5): 697–714. Ellis, M., Durand, H. and Christofides, P. (2014). A tutorial review of economic model predictive control methods, Journal of Process Control 24 (8): 1156–1178. Gonzalez, A., Adam, E. and Marchetti, J. (2008). Conditions for offset elimination in state space receding horizon controllers: A tutorial analysis, Chemical Engineering and Processing 47 (12): 2184–2194. Kuure-Kinsey, M., Cutright, R. and Bequette, B. (2006). Computationally efficient neural predictive control

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Adaptive predictions of the euro/złoty currency exchange rate using state space wavelet networks and forecast combinations

References BIS (2013). Foreign Exchange Turnover in April 2013: Preliminary Global Results , Triennial Central Bank Survey, Bank of International Settlements, Basel. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31 (3): 307–327. Borowa, A., Brdyś, M.A. and Mazur, K. (2007). Modelling of wastewater treatment plant for monitoring and control purposes by state-space wavelet networks, International Journal of Computers, Communications & Control 2 (2): 121–131. Brdyś, M

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A State Space Formulation for the Evaluation of the Pounding Forces During Earthquake

Abstract

In recent years, the pounding effect during earthquake is a subject of high significance for structural engineers. In this paper, a state space formulation of the equation of motion is used in a MATLAB code. The pounding forces are calculated using nonlinear viscoelastic impact element. The numerical study is performed on SDOF structures subjected by 1940 EL-Centro and 1977 Vrancea N-S recording. While most of the studies available in the literature are related to Newmark implicit time integration method, in this study the equations of motion in state space form are direct integrated. The time domain is chosen instead of the complex one in order to catch the nonlinear behavior of the structures. The physical nonlinear behavior of the structures is modeled according to the Force Analogy Method. The coupling of the Force Analogy Method with the state space approach conducts to an explicit time integration method. Consequently, the collision is easily checked and the pounding forces are taken into account into the equation of motion in an easier manner than in an implicit integration method. A comparison with available data in the literature is presented.

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Fuzzy Adaptation in a State Space Controller Applied for a Two-Mass System

Abstract

Application of a state space controller for two-mass system has been examined. However, the classical version of the controller was modified in order to improve properties of the whole system. For this purpose fuzzy model was implemented as an adaptation element for the parameters. The theoretical description of the control structure, numerical tests and experimental results (using dSPACE1103 card) have been presented.

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Effect of Hall Current in Thermoelastic Materials with Double Porosity Structure

matrix approach thermoelasticity. - Proceedings of the Fifteenth Midwestern Mechanics Conference, Chicago, pp.161-163. [23] Bahar L.Y. and Hetnarski R.B. (1977b): Coupled thermoelasticity of layered medium. - Proceedings of the Fourteenth Annual Meeting of the Society of Engineering Science, Lehigh University, Bethlehem, PA, pp.813-816. [24] Bahar L.Y. and Hetnarski R.B. (1978): State space approach to thermoelasticity. - J. Therm. Stresses, vol.1, No.1, pp.135-145. [25] Bahar L.Y. and Hetnarski R.B. (1979): Connection

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A Memory–Efficient Noninteger–Order Discrete–Time State–Space Model of a Heat Transfer Process

. Oprzędkiewicz, K. (2003). The interval parabolic system, Archives of Control Sciences 13(4): 415-430. Oprzędkiewicz, K. (2004). A controllability problem for a class of uncertain parameters linear dynamic systems, Archives of Control Sciences 14(1): 85-100. Oprzędkiewicz, K. (2005). An observability problem for a class of uncertain-parameter linear dynamic systems, International Journal of Applied Mathematics and Computer Science 15(3): 331-338. Oprzędkiewicz, K. and Gawin, E. (2016). A noninteger order, state space model for

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Factor Structural Time Series Models for Official Statistics with an Application to Hours Worked in Germany

.01.009. Bernanke, B.S., J. Boivin, and P. Eliasz. 2005. “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach.” The Quarterly Journal of Economics 120(1): 387-422. Doi: https://doi.org/10.1162/0033553053327452. Bollineni-Balabay, O., J. van den Brakel, and F. Palm. 2015. “Multivariate State Space Approach to Variance Reduction in Series with Level and Variance Breaks Due to Survey Redesigns.” Journal of the Royal Statistical Society: Series A (Statistics in Society). Doi: http://dx.doi.org/10.1111/rssa.12117

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