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Sliding–window neural state estimation in a power plant heater line
Author(s) -
Alessandri A.,
Parisini T.,
Zoppoli R.
Publication year - 2001
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.657
Subject(s) - estimator , kalman filter , control theory (sociology) , artificial neural network , sliding window protocol , nonlinear system , feedforward neural network , computer science , state (computer science) , extended kalman filter , algorithm , mathematical optimization , mathematics , artificial intelligence , window (computing) , statistics , physics , control (management) , quantum mechanics , operating system
The state estimation problem for a section of a real power plant is addressed by means of a recently proposed sliding‐window neural state estimator. The complexity and the nonlinearity of the considered application prevent us from successfully using standard techniques as Kalman filtering. The statistics of the distribution of the initial state and of noises are assumed to be unknown and the estimator is designed by minimizing a given generalized least‐squares cost function. The following approximations are enforced: (i) the state estimator is a finite‐memory one, (ii) the estimation functions are given fixed structures in which a certain number of parameters have to be optimized (multilayer feedforward neural networks are chosen from among various possible nonlinear approximators), (iii) the algorithms for optimizing the parameters (i.e., the network weights) rely on a stochastic approximation. Extensive simulation results on a complex model of a part of a real power plant are reported to compare the behaviour of the proposed estimator with the extended Kalman filter. Copyright © 2001 John Wiley & Sons, Ltd.