Stochastic Configuration Networks: Fundamentals and Algorithms
By Dianhui Wang, Ming Li. IEEE Transactions on Cybernetics, 2017
638 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/57580ae7893c513ebd5eb1fabd196ce509030a8f
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In Artificial Intelligence
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- Pei Wang
- Vishal M. Patel
- Vishwanath A. Sindagi
- Chen Change Loy
- Jiashi Feng
- Zhaowei Cai
In Electrical and Electronic Engineering
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