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Expert Systems with Applications: An International Journal
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ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
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WSEAS TRANSACTIONS on SYSTEMS
A novel fast Kolmogorov's spline complex network for pattern detection
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Non-linear dynamic system identification using Cascaded Functional Link Artificial Neural Network
International Journal of Artificial Intelligence and Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Asymptotically stable adaptive critic design for uncertain nonlinear systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Output feedback control of a quadrotor UAV using neural networks
IEEE Transactions on Neural Networks
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ACMOS'07 Proceedings of the 9th WSEAS international conference on Automatic control, modelling and simulation
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ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Neural net backlash compensation with Hebbian tuning using dynamic inversion
Automatica (Journal of IFAC)
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Automatica (Journal of IFAC)
Mathematical and Computer Modelling: An International Journal
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Mathematical and Computer Modelling: An International Journal
Letters: Random optimized geometric ensembles
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International Journal of Intelligent Mechatronics and Robotics
Fast image classification algorithms based on random weights networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Expert Systems with Applications: An International Journal
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Information Sciences: an International Journal
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A theoretical justification for the random vector version of the functional-link (RVFL) net is presented in this paper, based on a general approach to adaptive function approximation. The approach consists of formulating a limit-integral representation of the function to be approximated and subsequently evaluating that integral with the Monte-Carlo method. Two main results are: (1) the RVFL is a universal approximator for continuous functions on bounded finite dimensional sets, and (2) the RVFL is an efficient universal approximator with the rate of approximation error convergence to zero of order O(C/√n), where n is number of basis functions and with C independent of n. Similar results are also obtained for neural nets with hidden nodes implemented as products of univariate functions or radial basis functions. Some possible ways of enhancing the accuracy of multivariate function approximations are discussed