Time series nonlinearity modeling: a Giannakis formula type approach
Signal Processing - Special section: Hans Wilhelm Schüßler celebrates his 75th birthday
Intelligent data analysis
A real-time neuro-adaptive controller with guaranteed stability
Applied Soft Computing
A two-stage design of adaptive fuzzy controllers for time-delay systems with unknown models
International Journal of Systems Science
Stabilization of unknown nonlinear systems using neural networks
Applied Soft Computing
A neural adaptive feedback linearization control for CSTR, using NARMA-L2 model
CSECS'06 Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing
Hybrid Neural Network Controller Using Adaptation Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Constrained Control of a Class of Uncertain Nonlinear MIMO Systems Using Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
On-Line Modeling Via Fuzzy Support Vector Machines
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Feedback Linearization Using Neural Networks: Application to an Electromechanical Process
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Overhead conductor thermal dynamics identification by using echo state networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
WSEAS Transactions on Circuits and Systems
International Journal of Applied Mathematics and Computer Science
Adaptive neural flight control system for helicopter
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
New robust-adaptive algorithm for tracking control of robot manipulators
International Journal of Robotics and Automation
RBF based induction motor control with a good nonlinearity compensation
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A distributed control framework for performance management of virtualized computing environments
Proceedings of the 7th international conference on Autonomic computing
Neural network based software system for hemodynamic simulation
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
An on-line learning neural controller for helicopters performing highly nonlinear maneuvers
Applied Soft Computing
Intelligent learning rules for fuzzy control of a vibrating screen
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Expert Systems with Applications: An International Journal
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Automatica (Journal of IFAC)
Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks
Automatica (Journal of IFAC)
Linear Quadratic Control for Quadrotors UAVs Dynamics and Formation Flight
Journal of Intelligent and Robotic Systems
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The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right