Incremental constructive ridgelet neural network

Authors:
Shuyuan Yang;Min Wang;Licheng Jiao
Affiliations:
Department of Electrical Engineering, Institute of Intelligence Information Processing, Xidian University, Xi'an, Shaanxi 710071, China;National Laboratory of Radar Signal Processing, Department of Electrical Engineering, Xidian University, Xi'an, Shaanxi 710071, China;Department of Electrical Engineering, Institute of Intelligence Information Processing, Xidian University, Xi'an, Shaanxi 710071, China
Venue:
Neurocomputing
Year:
2008

Citing 9
Cited 4

Genetic algorithms and classifier systems: foundations and future directions

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Universal approximation using radial-basis-function networks

Neural Computation
Image compression using multi-layer neural networks

ISCC '97 Proceedings of the 2nd IEEE Symposium on Computers and Communications (ISCC '97)
A new adaptive ridgelet neural network

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Universal approximation bounds for superpositions of a sigmoidal function

IEEE Transactions on Information Theory
The finite ridgelet transform for image representation

IEEE Transactions on Image Processing
Learning capability and storage capacity of two-hidden-layer feedforward networks

IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes

IEEE Transactions on Neural Networks
Wavelet networks

IEEE Transactions on Neural Networks

Extreme and incremental learning based single-hidden-layer regularization ridgelet network

Neurocomputing
A novel neural-network approach of analog fault diagnosis based on kernel discriminant analysis and particle swarm optimization

Applied Soft Computing
Online sequential extreme learning of sparse ridgelet kernel regressor for nonlinear time-series prediction

IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Sparse Ridgelet Kernel Regressor and its online sequential extreme learning

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, a new kind of neural network is proposed by combining ridgelet with feedforward neural network (FNN). The network adopts ridgelet as the activation function in the hidden layer, and an incremental constructive method is employed to determine the structure of the network. Since ridgelets are efficient in describing linear, curvilinear, and hyperplane like structures in high dimensions, accordingly the network can approximate quite a wide range of multivariate functions in a more stable and efficient way, especially those with certain kinds of spatial inhomogeneities. Moreover, the incremental extreme learning machine makes adding the hidden nodes one by one possible, and it only needs to adjust the output weights linking the hidden layer and the output layer when more hidden neurons are added. By defining the cost function as the difference between the previously approximated function and the currently approximating one, a genetic algorithm is used to determine the optimal directions of ridgelet neurons. The construction and learning of the network are presented in detail. The superiority of the proposed model is demonstrated by simulation experiments in function learning and image compression.