A method using the Hopfield neural network is developed for estimating the parameters of a nonlinear system whose theoretical model is assumed to exist. A linearization procedure is presented, and the errors between the dynamics of the plant and its model are minimized through a cost function that is equated to the energy function of a Hopfield neural network. The minimization process yields the weights and biases of the neural network. Proof of convergence of the modeled parameters to their true values and boundedness of parameter estimates at each step are provided. Numerical results from a scalar time-varying problem and a complex nine-state aircraft problem are presented to demonstrate the potential of this method
This paper presents an efficient approach based on a recurrent neural network for solving constraine...
This paper presents an efficient approach based on recurrent neural network for solving nonlinear op...
A method for the development of mathematical models for dynamic systems with arbitrary nonlinearitie...
A method using the Hopfield neural network is developed for estimating the parameters of a nonlinear...
The ability of neural networks to realize some complex nonlinear function makes them attractive for ...
In this document the analysis and results of application13; of Hopfield Neural Network for estimatio...
This paper presents a contribution to the use of Hopfield neural networks (HNNs) for parameter estim...
Various recurrent neural network architectures for solving the problems of parameter estimation in d...
[[abstract]]In this paper, an identification method is proposed for discrete-time nonlinear systems ...
Methods for estimating the aerospace system parameters and controlling them through two neural netwo...
High computation rates can be achieved using artificial neural networks. Optimization problems can b...
In this work, a novel method, based upon Hopfield neural networks, is proposed for parameter estimat...
This paper is concerned with the robust identification of linear model when the modeling error is as...
[[abstract]]This paper proposes a uniform method for identifying four model types of high-order disc...
This work presents a straightforward methodology based on neural networks (NN) which allows to obtai...
This paper presents an efficient approach based on a recurrent neural network for solving constraine...
This paper presents an efficient approach based on recurrent neural network for solving nonlinear op...
A method for the development of mathematical models for dynamic systems with arbitrary nonlinearitie...
A method using the Hopfield neural network is developed for estimating the parameters of a nonlinear...
The ability of neural networks to realize some complex nonlinear function makes them attractive for ...
In this document the analysis and results of application13; of Hopfield Neural Network for estimatio...
This paper presents a contribution to the use of Hopfield neural networks (HNNs) for parameter estim...
Various recurrent neural network architectures for solving the problems of parameter estimation in d...
[[abstract]]In this paper, an identification method is proposed for discrete-time nonlinear systems ...
Methods for estimating the aerospace system parameters and controlling them through two neural netwo...
High computation rates can be achieved using artificial neural networks. Optimization problems can b...
In this work, a novel method, based upon Hopfield neural networks, is proposed for parameter estimat...
This paper is concerned with the robust identification of linear model when the modeling error is as...
[[abstract]]This paper proposes a uniform method for identifying four model types of high-order disc...
This work presents a straightforward methodology based on neural networks (NN) which allows to obtai...
This paper presents an efficient approach based on a recurrent neural network for solving constraine...
This paper presents an efficient approach based on recurrent neural network for solving nonlinear op...
A method for the development of mathematical models for dynamic systems with arbitrary nonlinearitie...
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