Stochastic Configuration Networks: Fundamentals and Algorithms

This paper contributes to the development of randomized methods for neural networks. The proposed learner model is generated incrementally by stochastic configuration (SC) algorithms, termed SC networks (SCNs). In contrast to the existing randomized learning algorithms for single layer feed-forward...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 47; H. 10; S. 3466 - 3479
Hauptverfasser: Wang, Dianhui, Li, Ming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.10.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation algorithms
Classification
Computational modeling
Computer simulation
Data analytics
Data models
incremental learning
Machine learning
Machine learning algorithms
Neural networks
Randomization
randomized algorithms
Regression analysis
stochastic configuration networks (SCNs)
Stochastic processes
Training
universal approximation property
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:This paper contributes to the development of randomized methods for neural networks. The proposed learner model is generated incrementally by stochastic configuration (SC) algorithms, termed SC networks (SCNs). In contrast to the existing randomized learning algorithms for single layer feed-forward networks, we randomly assign the input weights and biases of the hidden nodes in the light of a supervisory mechanism, and the output weights are analytically evaluated in either a constructive or selective manner. As fundamentals of SCN-based data modeling techniques, we establish some theoretical results on the universal approximation property. Three versions of SC algorithms are presented for data regression and classification problems in this paper. Simulation results concerning both data regression and classification indicate some remarkable merits of our proposed SCNs in terms of less human intervention on the network size setting, the scope adaptation of random parameters, fast learning, and sound generalization.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2017.2734043