On-Line Learning Fokker-Planck Machine
Neural Processing Letters
Neural Net-Based H∞ Control for a Class of Nonlinear Systems
Neural Processing Letters
Chaos control and synchronization, with input saturation, via recurrent neural networks
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
H∞ Control Design Using Dynamic Neural Networks
Neural Processing Letters
Comparing Newton with ANN for load flow
Math'04 Proceedings of the 5th WSEAS International Conference on Applied Mathematics
International Journal of Systems Science - Advances in Sliding Mode Observation and Estimation (Part Two)
Control of unstable nonlinear and nonstationary systems using LAMSTAR neural networks
ISC '07 Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control
Neural block control for synchronous generators
Engineering Applications of Artificial Intelligence
Implementation of MPC as an AQM controller
Computer Communications
CA '07 Proceedings of the Ninth IASTED International Conference on Control and Applications
Identification of nonlinear systems using Polynomial Nonlinear State Space models
Automatica (Journal of IFAC)
Stationary Fokker: planck learning for the optimization of parameters in nonlinear models
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Reliable robust controller design for nonlinear state-delayed systems based on neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Prediction of technological parameters during polymer material grinding
ACMOS'11 Proceedings of the 13th WSEAS international conference on Automatic control, modelling & simulation
Robust stability of nonlinear neural-network modeled systems
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Robust h∞ control for delayed nonlinear systems based on standard neural network models
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Theory and application of artificial neural networks for the real time prediction of ship motion
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
On reverse engineering in the cognitive and brain sciences
Natural Computing: an international journal
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From the Publisher:Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are the universal approximation ability, the parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamical models that contain neural network architectures might be highly non-linear and as a result difficult to analyse. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. Both classical and novel network architectures and learning algorithms for modelling and control are presented. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NL[subscript q] Theory as an extension towards modern control theory in order to analyse and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition. Neural state space control systems are an example of this. Moreover, it turns out that NL[subscript q] Theory is unifying with respect to any problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NL[subscript q] Theory, including mastering chaos. The didactic flavour of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NL[subscript q] Theory, that have applications in contro