A stochastic configuration network based on chaotic sparrow search algorithm
Stochastic configuration network (SCN), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification problems. However, the accuracy of the SCN is affected by the assignation and selection of some network p...
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| Published in | Knowledge-based systems Vol. 220; p. 106924 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
23.05.2021
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Stochastic configuration network (SCN), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification problems. However, the accuracy of the SCN is affected by the assignation and selection of some network parameters significantly Sparrow search algorithm (SSA) is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of sparrow population. In this paper, a stochastic configuration network based on chaotic sparrow search algorithm is first introduced, termed as CSSA-SCN. Firstly, chaotic sparrow search algorithm (CSSA) is designed which mainly utilizes logistic mapping, self-adaptive hyper-parameters, mutation operator to enhance the global optimization capability of SSA; Secondly, as the performance of SCN is related to regularization parameter r and scale factor λ of weights and biases, then CSSA is employed to give better parameters for SCN automatically; Finally, 13 benchmark functions and several datasets are used to evaluate the performance of CSSA and CSSA-SCN respectively. Experimental results demonstrate the feasibility and validity of CSSA-SCN compared with SCN and other contrast algorithms. |
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| AbstractList | Stochastic configuration network (SCN), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification problems. However, the accuracy of the SCN is affected by the assignation and selection of some network parameters significantly Sparrow search algorithm (SSA) is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of sparrow population. In this paper, a stochastic configuration network based on chaotic sparrow search algorithm is first introduced, termed as CSSA-SCN. Firstly, chaotic sparrow search algorithm (CSSA) is designed which mainly utilizes logistic mapping, self-adaptive hyper-parameters, mutation operator to enhance the global optimization capability of SSA; Secondly, as the performance of SCN is related to regularization parameter r and scale factor λ of weights and biases, then CSSA is employed to give better parameters for SCN automatically; Finally, 13 benchmark functions and several datasets are used to evaluate the performance of CSSA and CSSA-SCN respectively. Experimental results demonstrate the feasibility and validity of CSSA-SCN compared with SCN and other contrast algorithms. |
| ArticleNumber | 106924 |
| Author | Ding, Shifei Zhang, Chenglong |
| Author_xml | – sequence: 1 givenname: Chenglong surname: Zhang fullname: Zhang, Chenglong organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 2 givenname: Shifei surname: Ding fullname: Ding, Shifei email: dingsf@cumt.edu.cn organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China |
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| SubjectTerms | Algorithms Chaotic Configurations Global optimization Heuristic methods Logistic mapping Mutation Optimization Parameters Performance evaluation Regularization Search algorithms Sparrow search algorithm Stochastic configuration network |
| Title | A stochastic configuration network based on chaotic sparrow search algorithm |
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