Abstract
A computationally efficient technique for the identification of the parameters of a deterministic linear dynamical model is introduced. The method is based on the state space formulation. The gradiant and Hessian of the objective function are computed recursively. The performance of the method is evaluated by modeling demand for a specific product with price as the input to the system. Other modeling and forecasting applications of the method are discussed. A brief description of the extension of the results to the stochastic system identification is given.
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This work was done while the author was with Bell Laboratories, Murray Hill, New Jersey, USA.
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Aminzadeh, F. Parameter identification of a linear dynamic model A state space approach. Zeitschrift für Operations Research 31, B73–B95 (1987). https://doi.org/10.1007/BF01258138
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DOI: https://doi.org/10.1007/BF01258138