The :20Brain-state-in-a-box" Neural model is a gradient descent algorithm
Journal of Mathematical Psychology
Nonlinear systems analysis (2nd ed.)
Nonlinear systems analysis (2nd ed.)
Stability and optimization analyses of the generalized brain-state-in-a-box neural network model
Journal of Mathematical Psychology
Some new trends in identification and modeling of nonlinear dynamical systems
Applied Mathematics and Computation - Special issue on dynamics and control
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Robot Dynamics and Control
Mathematical Methods for Neural Network Analysis and Design
Mathematical Methods for Neural Network Analysis and Design
Parametric identification of robotic systems with stable time-varying Hopfield networks
Neural Computing and Applications
Modelling the HIV-AIDS Cuban Epidemics with Hopfield Neural Networks
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Nonlinear Parametric Model Identification using Genetic Algorithms
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Estimation of the rate of detection of infected individuals in an epidemiological model
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Robustness of the "hopfield estimator" for identification of dynamical systems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Numerical implementation of gradient algorithms
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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In this work, a novel method, based upon Hopfield neural networks, is proposed for parameter estimation, in the context of system identification. The equation of the neural estimator stems from the applicability of Hopfield networks to optimization problems, but the weights and the biases of the resulting network are time-varying, since the target function also varies with time. Hence the stability of the method cannot be taken for granted. In order to compare the novel technique and the classical gradient method, simulations have been carried out for a linearly parameterized system, and results show that the Hopfield network is more efficient than the gradient estimator, obtaining lower error and less oscillations. Thus the neural method is validated as an on-line estimator of the time-varying parameters appearing in the model of a nonlinear physical system.