Universal approximation using radial-basis-function networks
Neural Computation
Approximation and radial-basis-function networks
Neural Computation
Neural Fuzzy Control Systems with Structure and Parameter Learning
Neural Fuzzy Control Systems with Structure and Parameter Learning
Radial basis function networks and complexity regularization in function learning
IEEE Transactions on Neural Networks
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
On-line learning with minimal degradation in feedforward networks
IEEE Transactions on Neural Networks
On-Line Tuning of a Neural PID Controller Based on Variable Structure RBF Network
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
An intelligent PID controller based on variable structure radial basis function network
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Radial basis function networks with hybrid learning for system identification with outliers
Applied Soft Computing
Expert Systems with Applications: An International Journal
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This paper presents an adaptive RBF network for the on-line identification and tracking of rapidly-changing time-varying nonlinear systems. The proposed algorithm is capable of maintaining the accuracy of learned patterns even when a large number of aged patterns are replaced by new ones through the adaptation process. Moreover, the algorithm exhibits a strong learning capacity with instant embodiment of new data which makes it suitable for tracking of fast-changing systems. However, the accuracy and speed in the adaptation is balanced by the computational cost which increases with the square of the number of the radial basis functions, resulting in a computational expensive, but still practically feasible, algorithm. The simulation results show the effectiveness (in terms of degradation of learned patterns and learning capacity) of this architecture for adaptive modeling.