Universal approximation using radial-basis-function networks
Neural Computation
Regularization theory and neural networks architectures
Neural Computation
A sparse representation for function approximation
Neural Computation
Geometrical multi-resolution network based on ridgelet frame
Signal Processing
Letters: Convex incremental extreme learning machine
Neurocomputing
Incremental constructive ridgelet neural network
Neurocomputing
Neurocomputing
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
A new adaptive ridgelet neural network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
The finite ridgelet transform for image representation
IEEE Transactions on Image Processing
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
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Based on the previous work on ridgelet neural network, which employs the ridgelet function as the activation function in a feedforward neural network, in this paper we proposed a single-hidden-layer regularization ridgelet network (SLRRN). An extra regular item indicating the prior knowledge of the problem to be solved is added in the cost functional to obtain better generalization performance, and a simple and efficient method named cost functional minimized extreme and incremental learning (CFM-EIL) algorithm is proposed. In CFM-EIL based SLRRN (CFM-EIL-SLRRN), the ridgelet hidden neurons together with their parameters are tuned incrementally and analytically; thus it can significantly reduce the computational complexity of gradient based or other iterative algorithms. Some simulation experiments about time-series forecasting are taken, and several commonly used regression ways are considered under the same condition to give a comparison result. The results show the superiority of the proposed CFM-EIL-SLRRN to its counterparts in forecasting.