Evaluation of China's regional innovation capability based on simulated annealing projection pursuit model and nested fuzzy evaluation model
Regional innovation capability is an important indicator of both regional innovative and long‐term development. The purpose of this study is to build an evaluation index system for regional innovation capability in order to identify regional differences and support innovation more effectively. After...
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| Veröffentlicht in: | Expert systems 2023-06, Vol.40 (5), p.n/a |
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| creator | Ge, Mina Lin, Hualiang |
| description | Regional innovation capability is an important indicator of both regional innovative and long‐term development. The purpose of this study is to build an evaluation index system for regional innovation capability in order to identify regional differences and support innovation more effectively. After establishing a reasonable evaluation value for regional innovation capability, a combination of simulated annealing optimized projection pursuit (SA–PP) and N‐layer nested fuzzy comprehensive evaluation models is used to assess China's regional innovation capabilities. The results show that the SA–PP model effectively mitigates the risk of erroneous evaluation results caused by index weight uncertainty, resulting in a more reasonable, robust, and intelligent assessment of regional innovation capability. Furthermore, the nested fuzzy comprehensive evaluation model is capable of easily resolving the evaluation factor set's heterogeneity and multilayer problems. The most significant influences on China's regional innovation capability are knowledge acquisition and enterprise innovation. The comprehensive score of the proposed combinational evaluation model manifests that provinces with strong regional innovation capabilities are mainly concentrated in the southeast coastal regions. The research results allow for precise weight determination and object ranking. |
| doi_str_mv | 10.1111/exsy.13179 |
| format | Article |
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The purpose of this study is to build an evaluation index system for regional innovation capability in order to identify regional differences and support innovation more effectively. After establishing a reasonable evaluation value for regional innovation capability, a combination of simulated annealing optimized projection pursuit (SA–PP) and N‐layer nested fuzzy comprehensive evaluation models is used to assess China's regional innovation capabilities. The results show that the SA–PP model effectively mitigates the risk of erroneous evaluation results caused by index weight uncertainty, resulting in a more reasonable, robust, and intelligent assessment of regional innovation capability. Furthermore, the nested fuzzy comprehensive evaluation model is capable of easily resolving the evaluation factor set's heterogeneity and multilayer problems. The most significant influences on China's regional innovation capability are knowledge acquisition and enterprise innovation. The comprehensive score of the proposed combinational evaluation model manifests that provinces with strong regional innovation capabilities are mainly concentrated in the southeast coastal regions. The research results allow for precise weight determination and object ranking.</description><identifier>ISSN: 0266-4720</identifier><identifier>EISSN: 1468-0394</identifier><identifier>DOI: 10.1111/exsy.13179</identifier><language>eng</language><publisher>Oxford: Blackwell Publishing Ltd</publisher><subject>Coastal zone ; Heterogeneity ; innovation capability ; Innovations ; intelligent evaluation ; Knowledge acquisition ; Multilayers ; nested fuzzy model ; optimization algorithm ; projection pursuit ; Regional analysis ; Regional development ; regional innovation ; Simulated annealing</subject><ispartof>Expert systems, 2023-06, Vol.40 (5), p.n/a</ispartof><rights>2022 John Wiley & Sons Ltd.</rights><rights>2023 John Wiley & Sons, Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3019-4ae468db043961250c20bb7c3670942ec9f94b4e3f8052b39b5355f674a290b53</citedby><cites>FETCH-LOGICAL-c3019-4ae468db043961250c20bb7c3670942ec9f94b4e3f8052b39b5355f674a290b53</cites><orcidid>0000-0001-5579-7801</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fexsy.13179$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fexsy.13179$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Ge, Mina</creatorcontrib><creatorcontrib>Lin, Hualiang</creatorcontrib><title>Evaluation of China's regional innovation capability based on simulated annealing projection pursuit model and nested fuzzy evaluation model</title><title>Expert systems</title><description>Regional innovation capability is an important indicator of both regional innovative and long‐term development. The purpose of this study is to build an evaluation index system for regional innovation capability in order to identify regional differences and support innovation more effectively. After establishing a reasonable evaluation value for regional innovation capability, a combination of simulated annealing optimized projection pursuit (SA–PP) and N‐layer nested fuzzy comprehensive evaluation models is used to assess China's regional innovation capabilities. The results show that the SA–PP model effectively mitigates the risk of erroneous evaluation results caused by index weight uncertainty, resulting in a more reasonable, robust, and intelligent assessment of regional innovation capability. Furthermore, the nested fuzzy comprehensive evaluation model is capable of easily resolving the evaluation factor set's heterogeneity and multilayer problems. The most significant influences on China's regional innovation capability are knowledge acquisition and enterprise innovation. The comprehensive score of the proposed combinational evaluation model manifests that provinces with strong regional innovation capabilities are mainly concentrated in the southeast coastal regions. The research results allow for precise weight determination and object ranking.</description><subject>Coastal zone</subject><subject>Heterogeneity</subject><subject>innovation capability</subject><subject>Innovations</subject><subject>intelligent evaluation</subject><subject>Knowledge acquisition</subject><subject>Multilayers</subject><subject>nested fuzzy model</subject><subject>optimization algorithm</subject><subject>projection pursuit</subject><subject>Regional analysis</subject><subject>Regional development</subject><subject>regional innovation</subject><subject>Simulated annealing</subject><issn>0266-4720</issn><issn>1468-0394</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLAzEQx4MoWKsXP0HAgyBszWuzm6OU-oCCBxX0FLK72ZqSZtekW91-Bj-0aVfw5lyGmfnN6w_AOUYTHO1af4V-ginOxAEYYcbzBFHBDsEIEc4TlhF0DE5CWCKEcJbxEfiebZTt1No0DjY1nL4bpy4D9HoRM8pC41yzGcqlalVhrFn3sFBBVzDmgll1Vq1joJzTyhq3gK1vlrrct7SdD51Zw1VTaRuRCjoddnTdbbc91H-798QpOKqVDfrs14_By-3seXqfzB_vHqY386SkCIuEKR0_qwrEqOCYpKgkqCiykvIMCUZ0KWrBCqZpnaOUFFQUKU3TmmdMEYFiMAYXw9x46kcXL5LLpvPx3SBJjnGOMOY8UlcDVfomBK9r2XqzUr6XGMmd2nKnttyrHWE8wJ_G6v4fUs5en96Gnh9dLYTu</recordid><startdate>202306</startdate><enddate>202306</enddate><creator>Ge, Mina</creator><creator>Lin, Hualiang</creator><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-5579-7801</orcidid></search><sort><creationdate>202306</creationdate><title>Evaluation of China's regional innovation capability based on simulated annealing projection pursuit model and nested fuzzy evaluation model</title><author>Ge, Mina ; Lin, Hualiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3019-4ae468db043961250c20bb7c3670942ec9f94b4e3f8052b39b5355f674a290b53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Coastal zone</topic><topic>Heterogeneity</topic><topic>innovation capability</topic><topic>Innovations</topic><topic>intelligent evaluation</topic><topic>Knowledge acquisition</topic><topic>Multilayers</topic><topic>nested fuzzy model</topic><topic>optimization algorithm</topic><topic>projection pursuit</topic><topic>Regional analysis</topic><topic>Regional development</topic><topic>regional innovation</topic><topic>Simulated annealing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ge, Mina</creatorcontrib><creatorcontrib>Lin, Hualiang</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ge, Mina</au><au>Lin, Hualiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of China's regional innovation capability based on simulated annealing projection pursuit model and nested fuzzy evaluation model</atitle><jtitle>Expert systems</jtitle><date>2023-06</date><risdate>2023</risdate><volume>40</volume><issue>5</issue><epage>n/a</epage><issn>0266-4720</issn><eissn>1468-0394</eissn><abstract>Regional innovation capability is an important indicator of both regional innovative and long‐term development. The purpose of this study is to build an evaluation index system for regional innovation capability in order to identify regional differences and support innovation more effectively. After establishing a reasonable evaluation value for regional innovation capability, a combination of simulated annealing optimized projection pursuit (SA–PP) and N‐layer nested fuzzy comprehensive evaluation models is used to assess China's regional innovation capabilities. The results show that the SA–PP model effectively mitigates the risk of erroneous evaluation results caused by index weight uncertainty, resulting in a more reasonable, robust, and intelligent assessment of regional innovation capability. Furthermore, the nested fuzzy comprehensive evaluation model is capable of easily resolving the evaluation factor set's heterogeneity and multilayer problems. The most significant influences on China's regional innovation capability are knowledge acquisition and enterprise innovation. 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| source | EBSCOhost Business Source Complete; Access via Wiley Online Library |
| subjects | Coastal zone Heterogeneity innovation capability Innovations intelligent evaluation Knowledge acquisition Multilayers nested fuzzy model optimization algorithm projection pursuit Regional analysis Regional development regional innovation Simulated annealing |
| title | Evaluation of China's regional innovation capability based on simulated annealing projection pursuit model and nested fuzzy evaluation model |
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