Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine

Authors:
Tiago Matias;Francisco Souza;Rui Araújo;Carlos Henggeler Antunes
Affiliations:
-;-;-;-
Venue:
Neurocomputing
Year:
2014

Citing 17
Cited 0

Multilayer feedforward networks are universal approximators

Neural Networks
SA-Prop: Optimization of Multilayer Perceptron Parameters Using Simulated Annealing

IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
Practical Genetic Algorithms with CD-ROM

Practical Genetic Algorithms with CD-ROM
Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms

Neural Networks
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)

Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
A Combined Genetic Algorithm and Orthogonal Transformation for Designing Feedforward Neural Networks

ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Differential Evolution and Levenberg Marquardt Trained Neural Network Scheme for Nonlinear System Identification

Neural Processing Letters
Non-uniform attractor embedding for time series forecasting by fuzzy inference systems

Neurocomputing
Rapid and brief communication: Evolutionary extreme learning machine

Pattern Recognition
OP-ELM: optimally pruned extreme learning machine

IEEE Transactions on Neural Networks
Deterministic local alignment methods improved by a simple genetic algorithm

Neurocomputing
Nonlinear system identification using memetic differential evolution trained neural networks

Neurocomputing
Hybrid Taguchi-genetic algorithm for global numerical optimization

IEEE Transactions on Evolutionary Computation
Rules extraction from constructively trained neural networks based on genetic algorithms

Neurocomputing
Tuning of the structure and parameters of a neural network using an improved genetic algorithm

IEEE Transactions on Neural Networks
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm

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

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper proposes a learning framework for single-hidden layer feedforward neural networks (SLFN) called optimized extreme learning machine (O-ELM). In O-ELM, the structure and the parameters of the SLFN are determined using an optimization method. The output weights, like in the batch ELM, are obtained by a least squares algorithm, but using Tikhonov's regularization in order to improve the SLFN performance in the presence of noisy data. The optimization method is used to the set of input variables, the hidden-layer configuration and bias, the input weights and Tikhonov's regularization factor. The proposed framework has been tested with three optimization methods (genetic algorithms, simulated annealing, and differential evolution) over 16 benchmark problems available in public repositories.