Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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01 Jan 1980
TL;DR: In this paper, a general class of parameter estimation methods for stochastic dynamical systems is studied and the class contains the least squares method, output-error methods, the maximum likelihood method and several other techniques.
Abstract: A general class of parameter estimation methods for stochastic dynamical systems is studied. The class contains the least squares method, output-error methods, the maximum likelihood method and several other techniques. It is shown that the class of estimates so obtained are asymptotically normal and expressions for the resulting asymptotic covariance matrices are given. The regularity conditions that are imposed to obtain these results, are fairly weak. It is, for example, not assumed that the true system can be described within the chosen model set, and, as a consequence, the results in this paper form a part of the so-called approximate modeling approach to system identification. It is also noteworthy that arbitrary feedback from observed system outputs to observed system inputs is allowed and stationarity is not required
162 citations
TL;DR: In this paper, the Hammerstein nonlinear system approach is used for identification of a DC motor rotating in two directions with real-time experiments, and the major nonlinearities, such as Coulomb friction and dead zone, are investigated and integrated in the nonlinear model.
162 citations
TL;DR: The passivity approach is applied to access several new stable properties of neuro identification and it is concluded that the gradient descent algorithm for weight adjustment is stable in an L(infinity) sense and robust to any bounded uncertainties.
Abstract: Nonlinear system online identification via dynamic neural networks is studied in this paper. The main contribution of the paper is that the passivity approach is applied to access several new stable properties of neuro identification. The conditions for passivity, stability, asymptotic stability, and input-to-state stability are established in certain senses. We conclude that the gradient descent algorithm for weight adjustment is stable in an L/sub /spl infin// sense and robust to any bounded uncertainties.
161 citations
TL;DR: In this article, a concurrent learning (CL)-based implementation of model-based RL to solve approximate optimal regulation problems online under a PE-like rank condition was developed, based on the observation that, given a model of the system, RL can be implemented by evaluating the Bellman error at any number of desired points in the state space.
161 citations
TL;DR: This paper presents a methodology for finding optimal system parameters and optimal control parameters using a novel adaptive particle swarm optimization (APSO) algorithm to achieve faster convergence speed and better solution accuracy with minimum incremental computational burden.
161 citations