Generalized single-hidden layer feedforward networks

Authors:
Ning Wang;Min Han;Guifeng Yu;Meng Joo Er;Fanchao Meng;Shulei Sun
Affiliations:
Marine Engineering College, Dalian Maritime University, Dalian, China;Faculty of EIEE, Dalian University of Technology, Dalian, China;Marine Engineering College, Dalian Maritime University, Dalian, China;Marine Engineering College, Dalian Maritime University, Dalian, China,School of EEE, Nanyang Technological University, Singapore;Marine Engineering College, Dalian Maritime University, Dalian, China;Marine Engineering College, Dalian Maritime University, Dalian, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

Citing 7
Cited 0

A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks

Neurocomputing
A Generalized Ellipsoidal Basis Function Based Online Self-constructing Fuzzy Neural Network

Neural Processing Letters
Capabilities of a four-layered feedforward neural network: four layers versus three

IEEE Transactions on Neural Networks
Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions

IEEE Transactions on Neural Networks
Smooth function approximation using neural networks

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

IEEE Transactions on Neural Networks
Estimating the Number of Hidden Neurons in a Feedforward Network Using the Singular Value Decomposition

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a novel generalized single-hidden layer feedforward network (GSLFN) by employing polynomial functions of inputs as output weights connecting randomly generated hidden units with corresponding output nodes. The main contributions are as follows. For arbitrary N distinct observations with n-dimensional inputs, the augmented hidden node output matrix of the GSLFN with L hidden nodes using any infinitely differentiable activation functions consists of L sub-matrix blocks where each includes n+1 column vectors. The rank of the augmented hidden output matrix is proved to be no less than that of the SLFN, and thereby contributing to higher approximation performance. Furthermore, under minor constraints on input observations, we rigorously prove that the GLSFN with L hidden nodes can exactly learn L(n+1) arbitrary distinct observations which is n+1 times what the SLFN can learn. If the approximation error is allowed, by means of the optimization of output weight coefficients, the GSLFN may require less than N/(n+1) random hidden nodes to estimate targets with high accuracy. Theoretical results of the GSLFN evidently perform significant superiority to that of SLFNs.