Orthogonal stochastic configuration networks with adaptive construction parameter for data analytics

Author:

Dai Wei,Ning Chuanfeng,Pei Shiyu,Zhu Song,Wang Xuesong

Abstract

AbstractAs a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning. However, the randomness makes SCNs more likely to generate approximate linear correlative nodes that are redundant and low quality, thereby resulting in non-compact network structure. In light of a fundamental principle in machine learning, that is, a model with fewer parameters holds improved generalization. This paper proposes orthogonal SCN, termed OSCN, to filtrate out the low-quality hidden nodes for network structure reduction by incorporating Gram–Schmidt orthogonalization technology. The universal approximation property of OSCN and an adaptive setting for the key construction parameters have been presented in details. In addition, an incremental updating scheme is developed to dynamically determine the output weights, contributing to improved computational efficiency. Finally, experimental results on two numerical examples and several real-world regression and classification datasets substantiate the effectiveness and feasibility of the proposed approach.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries

Postgraduate Research & Practice Innovation Program of Jiangsu Province

Publisher

Springer Science and Business Media LLC

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