A self-organizing RBF neural network based on distance concentration immune algorithm
Radial basis function neural network ( RBFNN ) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm ( DCIA &#...
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| Veröffentlicht in: | IEEE/CAA journal of automatica sinica 2020-01, Vol.7 (1), p.276-291 |
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| container_title | IEEE/CAA journal of automatica sinica |
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| creator | Qiao, Junfei Li, Fei Yang, Cuili Li, Wenjing Gu, Ke |
| description | Radial basis function neural network ( RBFNN ) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm ( DCIA ) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly, the information processing strength ( IPS ) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm ( DCIA-SORBFNN ) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIA-SORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors. |
| doi_str_mv | 10.1109/JAS.2019.1911852 |
| format | Article |
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How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm ( DCIA ) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly, the information processing strength ( IPS ) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm ( DCIA-SORBFNN ) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIA-SORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.]]></description><identifier>ISSN: 2329-9266</identifier><identifier>EISSN: 2329-9274</identifier><identifier>DOI: 10.1109/JAS.2019.1911852</identifier><identifier>CODEN: IJASJC</identifier><language>eng</language><publisher>Piscataway: Chinese Association of Automation (CAA)</publisher><subject>Algorithms ; Antibodies ; Approximation algorithms ; Biological neural networks ; Convergence ; Data processing ; Immune system ; IP networks ; Neural networks ; Neurons ; Nonlinear systems ; Parameter identification ; Prediction algorithms ; Radial basis function ; System identification</subject><ispartof>IEEE/CAA journal of automatica sinica, 2020-01, Vol.7 (1), p.276-291</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c373t-d09cdf0f1ab3b37e2de9ab6e98998287cfd3407a168fcf03ee0c8d3cfb5743c63</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8945495$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8945495$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Qiao, Junfei</creatorcontrib><creatorcontrib>Li, Fei</creatorcontrib><creatorcontrib>Yang, Cuili</creatorcontrib><creatorcontrib>Li, Wenjing</creatorcontrib><creatorcontrib>Gu, Ke</creatorcontrib><title>A self-organizing RBF neural network based on distance concentration immune algorithm</title><title>IEEE/CAA journal of automatica sinica</title><addtitle>JAS</addtitle><description><![CDATA[Radial basis function neural network ( RBFNN ) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm ( DCIA ) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly, the information processing strength ( IPS ) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm ( DCIA-SORBFNN ) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIA-SORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.]]></description><subject>Algorithms</subject><subject>Antibodies</subject><subject>Approximation algorithms</subject><subject>Biological neural networks</subject><subject>Convergence</subject><subject>Data processing</subject><subject>Immune system</subject><subject>IP networks</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Nonlinear systems</subject><subject>Parameter identification</subject><subject>Prediction algorithms</subject><subject>Radial basis function</subject><subject>System identification</subject><issn>2329-9266</issn><issn>2329-9274</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1LAzEQxRdRsNTeBS8Bz1vztZvNsRbrBwVB7Tlks5Oauk1qsovoX--Wll7mDcN78-CXZdcETwnB8u5l9j6lmMgpkYRUBT3LRpRRmUsq-PlpL8vLbJLSBmNMaCFKyUfZaoYStDYPca29-3N-jd7uF8hDH3U7SPcT4heqdYIGBY8alzrtDSAThum7qDs3nN1223tAul2H6LrP7VV2YXWbYHLUcbZaPHzMn_Ll6-PzfLbMDROsyxssTWOxJbpmNRNAG5C6LkFWUla0EsY2jGOhSVlZYzEDwKZqmLF1ITgzJRtnt4e_uxi-e0id2oQ--qFSUcZYwQnmYnDhg8vEkFIEq3bRbXX8VQSrPT818FN7furIb4jcHCIOAE72SvKCy4L9A2eobIw</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Qiao, Junfei</creator><creator>Li, Fei</creator><creator>Yang, Cuili</creator><creator>Li, Wenjing</creator><creator>Gu, Ke</creator><general>Chinese Association of Automation (CAA)</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm ( DCIA ) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly, the information processing strength ( IPS ) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm ( DCIA-SORBFNN ) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIA-SORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.]]></abstract><cop>Piscataway</cop><pub>Chinese Association of Automation (CAA)</pub><doi>10.1109/JAS.2019.1911852</doi><tpages>16</tpages></addata></record> |
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| subjects | Algorithms Antibodies Approximation algorithms Biological neural networks Convergence Data processing Immune system IP networks Neural networks Neurons Nonlinear systems Parameter identification Prediction algorithms Radial basis function System identification |
| title | A self-organizing RBF neural network based on distance concentration immune algorithm |
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