An adaptive recurrent fuzzy system for nonlinear identification
Applied Soft Computing
Adaptive control of a nonlinear dc motor drive using recurrent neural networks
Applied Soft Computing
Locally recurrent neural networks for wind speed prediction using spatial correlation
Information Sciences: an International Journal
Improved GAP-RBF network for classification problems
Neurocomputing
Adjoint EKF learning in recurrent neural networks for nonlinear active noise control
Applied Soft Computing
Optimization of rational-powered membership functions using extended Kalman filter
Fuzzy Sets and Systems
Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors
Advanced Engineering Informatics
H∞ estimation for fuzzy membership function optimization
International Journal of Approximate Reasoning
Multiple fuzzy neural networks modeling with sparse data
Neurocomputing
A comparison of networked approximators in parallel mode identification of a bioreactor
Advances in Engineering Software
Expert Systems with Applications: An International Journal
A versatile software tool making best use of sparse data for closed loop process control
Advances in Engineering Software
Online learning neural tracker
Neurocomputing
Pattern Recognition
A context layered locally recurrent neural network for dynamic system identification
Engineering Applications of Artificial Intelligence
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Customized prediction of respiratory motion with clustering from multiple patient interaction
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Although the potential of the powerful mapping and representational capabilities of recurrent network architectures is generally recognized by the neural network research community, recurrent neural networks have not been widely used for the control of nonlinear dynamical systems, possibly due to the relative ineffectiveness of simple gradient descent training algorithms. Developments in the use of parameter-based extended Kalman filter algorithms for training recurrent networks may provide a mechanism by which these architectures will prove to be of practical value. This paper presents a decoupled extended Kalman filter (DEKF) algorithm for training of recurrent networks with special emphasis on application to control problems. We demonstrate in simulation the application of the DEKF algorithm to a series of example control problems ranging from the well-known cart-pole and bioreactor benchmark problems to an automotive subsystem, engine idle speed control. These simulations suggest that recurrent controller networks trained by Kalman filter methods can combine the traditional features of state-space controllers and observers in a homogeneous architecture for nonlinear dynamical systems, while simultaneously exhibiting less sensitivity than do purely feedforward controller networks to changes in plant parameters and measurement noise