Papers by Keyword: Wiener Model

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Authors: Wen Jun Su, Hai Tao Chen
Abstract: Traditional estimation methods have poor performance for long-term data forecast. Using Wiener model to estimate, power spectral density of the input signal, and cross-spectral density of the input and output signals are needed, that are difficult to obtain. And the large amount of calculation is needed using Wiener model. Using AR model and Kalman model, estimated results tend to mean of the training set while the estimated distance increases. For these cases, a new algorithm for long-term estimation based on AR model, named sampling AR model, is presented. Grouping the training set and using a different group of the training set to estimate each value. Sampling AR model improves the accuracy of long-term estimation.
Authors: M. Montazeri-Gh, E. Mohammadi, S. Jafari
Abstract: This paper presents the application of Particle Swarm Optimization (PSO) algorithm for optimization of the Gas Turbine Engine (GTE) fuel control system. In this study, the Wiener model for GTE as a block structure model is firstly developed. This representation is an appropriate model for controller tuning. Subsequently, based on the nonlinear GTE nature, a Fuzzy Logic Controller (FLC) with an initial rule base is designed for the engine fuel system. Then, the initial FLC is tuned by PSO with emphasis on the engine safety and time response. In this study, the optimization process is performed in two stages during which the Data Base (DB) and the Rule Base (RB) of the initial FLC are tuned sequentially. The results obtained from the simulation show the ability of the approach to achieve an acceptable time response and to attain a safe operation by limiting the turbine rotor acceleration.
Authors: Arash Bahar, Ali Chaibakhsh, Sajad Haqdadi
Abstract: The Magneto-rheological (MR) dampers are favorite mechanical system in dynamic structures. This paper presents an application of Wiener-type nonlinear models for describing the hysteresis behaviors of MR dampers at different operating conditions. In this structure, a linear part consisting discrete-time Kautz filters is cascading by a nonlinear mapping function (feedforward neural network (FFNN)). The pole parameters of Kautz filters were chosen with respect to the poles of best fitted linear model on real system. By defining the parameters of Kautz filter, the nonlinear behaviors of system were identified using neural network model, as the output of filters were considered as the output on NN. In order to assess the performances of the developed models a comparison between the responses of the models and another recent modeling approach was preformed.
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