Brief paper: Adaptive critic methods for stochastic systems with input-dependent noise
Automatica (Journal of IFAC)
Adaptive control of a nonlinear dc motor drive using recurrent neural networks
Applied Soft Computing
Multi-objective optimization of TSK fuzzy models
Expert Systems with Applications: An International Journal
Prediction of a Lorenz chaotic attractor using two-layer perceptron neural network
Applied Soft Computing
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Stable adaptive control with recurrent networks
Automatica (Journal of IFAC)
Book review: Neural and adaptive systems: fundamentals through simulations
Automatica (Journal of IFAC)
Approximate models for nonlinear dynamical systems and their generalization properties
Mathematical and Computer Modelling: An International Journal
Identification of nonlinear dynamics using a general spatio-temporal network
Mathematical and Computer Modelling: An International Journal
An optimization-oriented view of random early detection
Computer Communications
A context layered locally recurrent neural network for dynamic system identification
Engineering Applications of Artificial Intelligence
Statistical and incremental methods for neural models selection
International Journal of Artificial Intelligence and Soft Computing
Hi-index | 0.01 |
An extension of the backpropagation method, termed dynamic backpropagation, which can be applied in a straightforward manner for the optimization of the weights (parameters) of multilayer neural networks is discussed. The method is based on the fact that gradient methods used in linear dynamical systems can be combined with backpropagation methods for neural networks to obtain the gradient of a performance index of nonlinear dynamical systems. The method can be applied to any complex system which can be expressed as the interconnection of linear dynamical systems and multilayer neural networks. To facilitate the practical implementation of the proposed method, emphasis is placed on the diagrammatic representation of the system which generates the gradient of the performance function