Design of a modular neural network based on an improved soft subspace clustering algorithm
Being a commonly used way for task decomposition in modular neural network (MNN), clustering analysis is employed to decompose the complex task into several simple subtasks for learning. Recent studies mainly focus on hard clustering, but the clusters might be not sufficiently represented when the c...
Uloženo v:
| Vydáno v: | Expert systems with applications Ročník 209; s. 118219 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
15.12.2022
|
| Témata: | |
| ISSN: | 0957-4174, 1873-6793 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Being a commonly used way for task decomposition in modular neural network (MNN), clustering analysis is employed to decompose the complex task into several simple subtasks for learning. Recent studies mainly focus on hard clustering, but the clusters might be not sufficiently represented when the cluster boundary is ambiguous, which may degenerate the learning performance of subnetworks in MNN. To solve this problem, we design a modular neural network based on an improved soft subspace clustering (IESSC-MNN) algorithm in this study. Firstly, we propose an improved soft subspace clustering algorithm for task decomposition in MNN, which divides the original space into several interactive feature subspaces and allocates a weight item to each subspace to describe the contribution of the subtasks at the same time. Secondly, each RBF subnetwork is adaptively constructed using a structure growing strategy, and all subnetworks learning the corresponding subtask in parallel. Finally, all subnetworks’ outputs are integrated by weighted summation using the contribution weight of subnetworks. The simulation results of the proposed model on five benchmark data and a practical dataset indicate that IESSC-MNN improves the modeling accuracy and generalization performance with a simple structure when compared with other MNNs. |
|---|---|
| AbstractList | Being a commonly used way for task decomposition in modular neural network (MNN), clustering analysis is employed to decompose the complex task into several simple subtasks for learning. Recent studies mainly focus on hard clustering, but the clusters might be not sufficiently represented when the cluster boundary is ambiguous, which may degenerate the learning performance of subnetworks in MNN. To solve this problem, we design a modular neural network based on an improved soft subspace clustering (IESSC-MNN) algorithm in this study. Firstly, we propose an improved soft subspace clustering algorithm for task decomposition in MNN, which divides the original space into several interactive feature subspaces and allocates a weight item to each subspace to describe the contribution of the subtasks at the same time. Secondly, each RBF subnetwork is adaptively constructed using a structure growing strategy, and all subnetworks learning the corresponding subtask in parallel. Finally, all subnetworks’ outputs are integrated by weighted summation using the contribution weight of subnetworks. The simulation results of the proposed model on five benchmark data and a practical dataset indicate that IESSC-MNN improves the modeling accuracy and generalization performance with a simple structure when compared with other MNNs. |
| ArticleNumber | 118219 |
| Author | Qiao, Junfei Li, Meng Li, Wenjing |
| Author_xml | – sequence: 1 givenname: Meng surname: Li fullname: Li, Meng email: 15801005128@163.com organization: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China – sequence: 2 givenname: Wenjing surname: Li fullname: Li, Wenjing email: wenjing.li@bjut.edu.cn organization: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China – sequence: 3 givenname: Junfei orcidid: 0000-0002-1707-6074 surname: Qiao fullname: Qiao, Junfei email: adqiao@bjut.edu.cn organization: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China |
| BookMark | eNp9kL1OwzAUhS1UJNrCCzD5BRKu7fxKLKj8SpVYYGGxHOemuCR2ZTuteHtSlYmB6egM39U934LMrLNIyDWDlAErbrYphoNKOXCeMlZxVp-ROatKkRRlLWZkDnVeJhkrswuyCGELwEqAck4-7jGYjaWuo4oOrh175anF0at-inhw_os2KmBLnaXKUjPsvNtPNbgu0jA2Yac0Ut2PIaI3dkNVv3HexM_hkpx3qg949ZtL8v748LZ6TtavTy-ru3WiBUBMBOqmhjzLCpi-ajumC14XHUIBQmsosrasQAmuscpFVzOoFPBMAzR5JxRysSTV6a72LgSPndQmqmicjV6ZXjKQR0dyK4-O5NGRPDmaUP4H3XkzKP_9P3R7gnAatTfoZdAGrcbWeNRRts78h_8ApYWC-g |
| CitedBy_id | crossref_primary_10_1007_s41870_024_01829_7 crossref_primary_10_1007_s10489_024_05308_1 crossref_primary_10_1109_TII_2023_3342896 crossref_primary_10_1007_s00034_024_02898_6 crossref_primary_10_1007_s40815_024_01932_8 crossref_primary_10_1016_j_eswa_2023_119977 crossref_primary_10_1108_MIP_04_2023_0145 crossref_primary_10_1016_j_ins_2023_03_030 |
| Cites_doi | 10.1016/j.ins.2008.11.018 10.1016/j.apm.2010.03.027 10.1371/journal.pone.0187736 10.1371/journal.pcbi.1004128 10.1016/j.ins.2014.02.091 10.1109/TNNLS.2013.2295813 10.1109/TNNLS.2014.2315622 10.1016/j.jas.2014.07.017 10.1016/j.apcata.2010.01.033 10.1109/TNNLS.2015.2469673 10.1016/0888-613X(95)00059-P 10.14445/22312803/IJCTT-V10P119 10.1016/j.isatra.2019.11.015 10.1016/j.jhydrol.2010.05.040 10.1016/S0965-9978(02)00107-2 10.1109/TCYB.2017.2764744 10.1109/ACCESS.2019.2913017 10.1016/j.neucom.2008.10.020 10.1016/j.asoc.2009.06.007 10.1109/TNNLS.2015.2465174 10.1016/j.ins.2008.07.013 10.1016/j.ins.2012.02.027 10.1016/j.cmpb.2013.01.002 10.1109/TNNLS.2014.2315214 10.1016/j.patcog.2009.09.010 10.1016/j.neunet.2007.02.003 10.1109/TNNLS.2015.2411671 10.1016/j.neucom.2012.09.038 10.1073/pnas.1510619112 10.1109/TNN.2004.837779 10.1109/TNNLS.2012.2185059 10.1007/s41109-020-00332-9 10.1016/j.ins.2012.12.040 10.1016/j.measurement.2020.107887 10.1016/j.asoc.2006.01.009 10.1016/S0031-3203(00)00181-3 10.1109/TNNLS.2017.2650865 10.1109/TNNLS.2015.2404823 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier Ltd |
| Copyright_xml | – notice: 2022 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.eswa.2022.118219 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-6793 |
| ExternalDocumentID | 10_1016_j_eswa_2022_118219 S0957417422013720 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABBOA ABFNM ABMAC ABMVD ABUCO ABYKQ ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SDS SES SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABKBG ABUFD ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET SEW WUQ XPP ZMT ~HD |
| ID | FETCH-LOGICAL-c300t-3ecb9054460001df1c6296fe0603cc064d780a32ce853f9108a024c00b5f3ae23 |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000950627300003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Sat Nov 29 07:06:33 EST 2025 Tue Nov 18 22:36:16 EST 2025 Fri Feb 23 02:40:18 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Improved second-order algorithm RBF neural network Soft subspace clustering Modular neural network |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-3ecb9054460001df1c6296fe0603cc064d780a32ce853f9108a024c00b5f3ae23 |
| ORCID | 0000-0002-1707-6074 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_eswa_2022_118219 crossref_primary_10_1016_j_eswa_2022_118219 elsevier_sciencedirect_doi_10_1016_j_eswa_2022_118219 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-12-15 |
| PublicationDateYYYYMMDD | 2022-12-15 |
| PublicationDate_xml | – month: 12 year: 2022 text: 2022-12-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2022 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Pan, Zhou, Lu, Sun (b0155) 2019; 7 Bertolero, M. A., Thomas Yeo, B. T., & D’Esposito, M. (2015). The modular and integrative functional architecture of the human brain. Cao (b0035) 2017; 55 Ellefsen, Mouret, Clune (b0070) 2015; 11 Melin, Mancilla, Lopez, Mendoza (b0120) 2007; 7 Mendoza, Melín, Castillo (b0130) 2009; 9 Wang, Gao, Qiu (b0185) 2016; 27 Han, Wu, Zhang, Tian, Qiao (b0090) 2019; 49 Oh, Suen (b0150) 2002; 35 Xu, Jagannathan (b0205) 2015; 26 Wang, Der, Nasrabadi, Member (b0180) 1998; 7 Wang, Chen (b0190) 2013; 232 Chris Tseng, Almogahed (b0050) 2009; 72 Hidalgo, Castillo, Melin (b0095) 2009; 179 Molina-Vilaplana, Feliu-Batlle, López-Coronado (b0145) 2007; 20 Xie, Yu, Hewlett, Rozycki, Wilamowski (b0200) 2012; 23 Chandra (b0040) 2015; 26 Gong, Liu, Li, Cai, Su (b0075) 2015; 26 AlZubi, Alarifi, Al-Maitah (b0005) 2020; 161 Loo, C. K., Rajeswari, M., & Rao, M. V. C. (2004). Jiang, Zhao, Ren (b0105) 2003; 34 Zhao, Gao, Glotin, Wu (b0215) 2010; 34 Yu, Reiner, Xie, Bartczak, Wilamowski (b0210) 2014; 25 Cho (b0165) 1995; 13 Günay, Yildirim (b0080) 2010; 377 Han, Zhang, Hou, Qiao (b0085) 2016; 27 Hoori, Motai (b0100) 2018; 29 Mohammed, Lim (b0140) 2015; 26 Melin, Sánchez, Castillo (b0125) 2012; 197 Valdez, Melin, Castillo (b0170) 2014; 270 (6), 1378–1395. , . Wu, Chau, Fan (b0195) 2010; 389 Bora, Gupta (b0030) 2014; 10 Dua, Graff (b0065) 2019 (49), E6798–E6807. https://doi.org/10.1073/pnas.1510619112. Amer, Maul (b0010) 2019 Li, Li, Qiao, Guo (b0110) 2020; 100 Azar, El-Said, Hassanien (b0020) 2013; 111 Demšar (b0055) 2006; 7 Deng, Choi, Chung, Wang (b0060) 2010; 43 Mendoza, Melin, Licea (b0135) 2009; 179 Chen, Murata (b0045) 2020 Aprile, Castellano, Eramo (b0015) 2014; 50 Qiao, Zhang, Bo (b0160) 2014; 125 Velez, Clune (b0175) 2017; 12 Yu (10.1016/j.eswa.2022.118219_b0210) 2014; 25 Chandra (10.1016/j.eswa.2022.118219_b0040) 2015; 26 Dua (10.1016/j.eswa.2022.118219_b0065) 2019 Mendoza (10.1016/j.eswa.2022.118219_b0130) 2009; 9 Mohammed (10.1016/j.eswa.2022.118219_b0140) 2015; 26 Qiao (10.1016/j.eswa.2022.118219_b0160) 2014; 125 Aprile (10.1016/j.eswa.2022.118219_b0015) 2014; 50 Bora (10.1016/j.eswa.2022.118219_b0030) 2014; 10 Wang (10.1016/j.eswa.2022.118219_b0190) 2013; 232 10.1016/j.eswa.2022.118219_b0025 Wu (10.1016/j.eswa.2022.118219_b0195) 2010; 389 AlZubi (10.1016/j.eswa.2022.118219_b0005) 2020; 161 Chris Tseng (10.1016/j.eswa.2022.118219_b0050) 2009; 72 Wang (10.1016/j.eswa.2022.118219_b0185) 2016; 27 Hidalgo (10.1016/j.eswa.2022.118219_b0095) 2009; 179 Gong (10.1016/j.eswa.2022.118219_b0075) 2015; 26 Demšar (10.1016/j.eswa.2022.118219_b0055) 2006; 7 Velez (10.1016/j.eswa.2022.118219_b0175) 2017; 12 Amer (10.1016/j.eswa.2022.118219_b0010) 2019 Günay (10.1016/j.eswa.2022.118219_b0080) 2010; 377 Oh (10.1016/j.eswa.2022.118219_b0150) 2002; 35 Molina-Vilaplana (10.1016/j.eswa.2022.118219_b0145) 2007; 20 Li (10.1016/j.eswa.2022.118219_b0110) 2020; 100 Cho (10.1016/j.eswa.2022.118219_b0165) 1995; 13 Melin (10.1016/j.eswa.2022.118219_b0120) 2007; 7 Wang (10.1016/j.eswa.2022.118219_b0180) 1998; 7 Xu (10.1016/j.eswa.2022.118219_b0205) 2015; 26 10.1016/j.eswa.2022.118219_b0115 Cao (10.1016/j.eswa.2022.118219_b0035) 2017; 55 Ellefsen (10.1016/j.eswa.2022.118219_b0070) 2015; 11 Azar (10.1016/j.eswa.2022.118219_b0020) 2013; 111 Jiang (10.1016/j.eswa.2022.118219_b0105) 2003; 34 Zhao (10.1016/j.eswa.2022.118219_b0215) 2010; 34 Deng (10.1016/j.eswa.2022.118219_b0060) 2010; 43 Hoori (10.1016/j.eswa.2022.118219_b0100) 2018; 29 Xie (10.1016/j.eswa.2022.118219_b0200) 2012; 23 Melin (10.1016/j.eswa.2022.118219_b0125) 2012; 197 Chen (10.1016/j.eswa.2022.118219_b0045) 2020 Mendoza (10.1016/j.eswa.2022.118219_b0135) 2009; 179 Han (10.1016/j.eswa.2022.118219_b0090) 2019; 49 Pan (10.1016/j.eswa.2022.118219_b0155) 2019; 7 Valdez (10.1016/j.eswa.2022.118219_b0170) 2014; 270 Han (10.1016/j.eswa.2022.118219_b0085) 2016; 27 |
| References_xml | – volume: 49 start-page: 69 year: 2019 end-page: 82 ident: b0090 article-title: Self-organizing RBF neural network using an adaptive gradient multiobjective particle swarm optimization publication-title: IEEE Transactions on Cybernetics – reference: Bertolero, M. A., Thomas Yeo, B. T., & D’Esposito, M. (2015). The modular and integrative functional architecture of the human brain. – volume: 55 start-page: 616 year: 2017 end-page: 624 ident: b0035 article-title: Research on traffic flow prediction method based on BP neural network publication-title: Boletin Tecnico/Technical Bulletin – volume: 7 start-page: 1 year: 2006 end-page: 30 ident: b0055 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: Journal of Machine Learning Research – year: 2019 ident: b0010 article-title: Reducing catastrophic forgetting in modular neural networks by dynamic information balancing publication-title: ArXiv – volume: 161 year: 2020 ident: b0005 article-title: Deep brain simulation wearable IoT sensor device based Parkinson brain disorder detection using heuristic tubu optimized sequence modular neural network publication-title: Measurement: Journal of the International Measurement Confederation – reference: , – volume: 377 start-page: 174 year: 2010 end-page: 180 ident: b0080 article-title: Analysis of selective CO oxidation over promoted Pt/Al publication-title: Applied Catalysis A: General – volume: 43 start-page: 767 year: 2010 end-page: 781 ident: b0060 article-title: Enhanced soft subspace clustering integrating within-cluster and between-cluster information publication-title: Pattern Recognition – volume: 34 start-page: 17 year: 2003 end-page: 24 ident: b0105 article-title: Design of structural modular neural networks with genetic algorithm publication-title: Advances in Engineering Software – volume: 10 start-page: 108 year: 2014 end-page: 113 ident: b0030 article-title: A comparative study between fuzzy clustering algorithm and hard clustering algorithm publication-title: International Journal of Computer Trends and Technology – volume: 27 start-page: 402 year: 2016 end-page: 415 ident: b0085 article-title: Nonlinear model predictive control based on a self-organizing recurrent neural network publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 72 start-page: 2093 year: 2009 end-page: 2100 ident: b0050 article-title: Modular neural networks with applications to pattern profiling problems publication-title: Neurocomputing – volume: 9 start-page: 1377 year: 2009 end-page: 1387 ident: b0130 article-title: Interval type-2 fuzzy logic and modular neural networks for face recognition applications publication-title: Applied Soft Computing Journal – volume: 20 start-page: 631 year: 2007 end-page: 645 ident: b0145 article-title: A modular neural network architecture for step-wise learning of grasping tasks publication-title: Neural Networks – volume: 197 start-page: 1 year: 2012 end-page: 19 ident: b0125 article-title: Genetic optimization of modular neural networks with fuzzy response integration for human recognition publication-title: Information Sciences – volume: 232 start-page: 116 year: 2013 end-page: 129 ident: b0190 article-title: Soft large margin clustering publication-title: Information Sciences – volume: 26 start-page: 3263 year: 2015 end-page: 3277 ident: b0075 article-title: A multiobjective sparse feature learning model for deep neural networks publication-title: IEEE Transactions on Neural Networks and Learning Systems – reference: Loo, C. K., Rajeswari, M., & Rao, M. V. C. (2004). – volume: 27 start-page: 416 year: 2016 end-page: 425 ident: b0185 article-title: A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 26 start-page: 3123 year: 2015 end-page: 3136 ident: b0040 article-title: Competition and collaboration in cooperative coevolution of elman recurrent neural networks for time-series prediction publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 100 start-page: 185 year: 2020 end-page: 197 ident: b0110 article-title: A feature clustering-based adaptive modular neural network for nonlinear system modeling publication-title: ISA Transactions – volume: 125 start-page: 7 year: 2014 end-page: 16 ident: b0160 article-title: An online self-adaptive modular neural network for time-varying systems publication-title: Neurocomputing – volume: 26 start-page: 417 year: 2015 end-page: 429 ident: b0140 article-title: An enhanced fuzzy min-max neural network for pattern classification publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 111 start-page: 1 year: 2013 end-page: 16 ident: b0020 article-title: Fuzzy and hard clustering analysis for thyroid disease publication-title: Computer Methods and Programs in Biomedicine – volume: 389 start-page: 146 year: 2010 end-page: 167 ident: b0195 article-title: Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques publication-title: Journal of Hydrology – volume: 270 start-page: 143 year: 2014 end-page: 153 ident: b0170 article-title: Modular Neural Networks architecture optimization with a new nature inspired method using a fuzzy combination of Particle Swarm Optimization and Genetic Algorithms publication-title: Information Sciences – volume: 50 start-page: 262 year: 2014 end-page: 272 ident: b0015 article-title: Combining image analysis and modular neural networks for classification of mineral inclusions and pores in archaeological potsherds publication-title: Journal of Archaeological Science – volume: 179 start-page: 2123 year: 2009 end-page: 2145 ident: b0095 article-title: Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms publication-title: Information Sciences – volume: 7 start-page: 1113 year: 1998 end-page: 1121 ident: b0180 article-title: Using a feature-decomposition and data-decomposition modular neural network publication-title: Image (Rochester, N.Y.) – reference: (49), E6798–E6807. https://doi.org/10.1073/pnas.1510619112. – reference: (6), 1378–1395. – volume: 179 start-page: 2078 year: 2009 end-page: 2101 ident: b0135 article-title: A hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral publication-title: Information Sciences – volume: 34 start-page: 3884 year: 2010 end-page: 3895 ident: b0215 article-title: A matrix modular neural network based on task decomposition with subspace division by adaptive affinity propagation clustering publication-title: Applied Mathematical Modelling – volume: 11 year: 2015 ident: b0070 article-title: Neural modularity helps organisms evolve to learn new skills without forgetting old skills publication-title: PLoS Computational Biology – volume: 7 start-page: 55807 year: 2019 end-page: 55816 ident: b0155 article-title: Prediction of network traffic of smart cities based on DE-BP neural network publication-title: IEEE Access – volume: 26 start-page: 472 year: 2015 end-page: 485 ident: b0205 article-title: Neural network-based finite horizon stochastic optimal control design for nonlinear networked control systems publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 35 start-page: 229 year: 2002 end-page: 244 ident: b0150 article-title: A class-modular feedforward neural network for handwriting recognition publication-title: Pattern Recognition – volume: 29 start-page: 766 year: 2018 end-page: 778 ident: b0100 article-title: Multicolumn RBF network publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 12 year: 2017 ident: b0175 article-title: Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks publication-title: PLoS One – volume: 23 start-page: 609 year: 2012 end-page: 619 ident: b0200 article-title: Fast and efficient second-order method for training radial basis function networks publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 7 start-page: 1217 year: 2007 end-page: 1226 ident: b0120 article-title: A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting publication-title: Applied Soft Computing Journal – volume: 13 start-page: 359 year: 1995 end-page: 375 ident: b0165 article-title: Fuzzy aggregation of modular neural networks with ordered weighted averaging operators publication-title: International Journal of Approximate Reasoning – year: 2019 ident: b0065 article-title: UCI machine learning repository – volume: 25 start-page: 1793 year: 2014 end-page: 1803 ident: b0210 article-title: An incremental design of radial basis function networks publication-title: IEEE Transactions on Neural Networks and Learning Systems – reference: . – year: 2020 ident: b0045 article-title: Enhancing network modularity to mitigate catastrophic forgetting publication-title: Applied Network Science – volume: 179 start-page: 2078 issue: 13 year: 2009 ident: 10.1016/j.eswa.2022.118219_b0135 article-title: A hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral publication-title: Information Sciences doi: 10.1016/j.ins.2008.11.018 – volume: 34 start-page: 3884 issue: 12 year: 2010 ident: 10.1016/j.eswa.2022.118219_b0215 article-title: A matrix modular neural network based on task decomposition with subspace division by adaptive affinity propagation clustering publication-title: Applied Mathematical Modelling doi: 10.1016/j.apm.2010.03.027 – volume: 12 issue: 11 year: 2017 ident: 10.1016/j.eswa.2022.118219_b0175 article-title: Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks publication-title: PLoS One doi: 10.1371/journal.pone.0187736 – volume: 11 issue: 4 year: 2015 ident: 10.1016/j.eswa.2022.118219_b0070 article-title: Neural modularity helps organisms evolve to learn new skills without forgetting old skills publication-title: PLoS Computational Biology doi: 10.1371/journal.pcbi.1004128 – volume: 270 start-page: 143 year: 2014 ident: 10.1016/j.eswa.2022.118219_b0170 article-title: Modular Neural Networks architecture optimization with a new nature inspired method using a fuzzy combination of Particle Swarm Optimization and Genetic Algorithms publication-title: Information Sciences doi: 10.1016/j.ins.2014.02.091 – volume: 25 start-page: 1793 issue: 10 year: 2014 ident: 10.1016/j.eswa.2022.118219_b0210 article-title: An incremental design of radial basis function networks publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2013.2295813 – volume: 26 start-page: 472 issue: 3 year: 2015 ident: 10.1016/j.eswa.2022.118219_b0205 article-title: Neural network-based finite horizon stochastic optimal control design for nonlinear networked control systems publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2014.2315622 – volume: 50 start-page: 262 year: 2014 ident: 10.1016/j.eswa.2022.118219_b0015 article-title: Combining image analysis and modular neural networks for classification of mineral inclusions and pores in archaeological potsherds publication-title: Journal of Archaeological Science doi: 10.1016/j.jas.2014.07.017 – volume: 377 start-page: 174 issue: 1–2 year: 2010 ident: 10.1016/j.eswa.2022.118219_b0080 article-title: Analysis of selective CO oxidation over promoted Pt/Al2O3 catalysts using modular neural networks: combining preparation and operational variables publication-title: Applied Catalysis A: General doi: 10.1016/j.apcata.2010.01.033 – volume: 26 start-page: 3263 issue: 12 year: 2015 ident: 10.1016/j.eswa.2022.118219_b0075 article-title: A multiobjective sparse feature learning model for deep neural networks publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2015.2469673 – volume: 13 start-page: 359 issue: 4 year: 1995 ident: 10.1016/j.eswa.2022.118219_b0165 article-title: Fuzzy aggregation of modular neural networks with ordered weighted averaging operators publication-title: International Journal of Approximate Reasoning doi: 10.1016/0888-613X(95)00059-P – volume: 10 start-page: 108 issue: 2 year: 2014 ident: 10.1016/j.eswa.2022.118219_b0030 article-title: A comparative study between fuzzy clustering algorithm and hard clustering algorithm publication-title: International Journal of Computer Trends and Technology doi: 10.14445/22312803/IJCTT-V10P119 – volume: 100 start-page: 185 year: 2020 ident: 10.1016/j.eswa.2022.118219_b0110 article-title: A feature clustering-based adaptive modular neural network for nonlinear system modeling publication-title: ISA Transactions doi: 10.1016/j.isatra.2019.11.015 – volume: 389 start-page: 146 issue: 1–2 year: 2010 ident: 10.1016/j.eswa.2022.118219_b0195 article-title: Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2010.05.040 – volume: 7 start-page: 1 year: 2006 ident: 10.1016/j.eswa.2022.118219_b0055 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: Journal of Machine Learning Research – volume: 34 start-page: 17 issue: 1 year: 2003 ident: 10.1016/j.eswa.2022.118219_b0105 article-title: Design of structural modular neural networks with genetic algorithm publication-title: Advances in Engineering Software doi: 10.1016/S0965-9978(02)00107-2 – volume: 49 start-page: 69 issue: 1 year: 2019 ident: 10.1016/j.eswa.2022.118219_b0090 article-title: Self-organizing RBF neural network using an adaptive gradient multiobjective particle swarm optimization publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2017.2764744 – volume: 7 start-page: 55807 year: 2019 ident: 10.1016/j.eswa.2022.118219_b0155 article-title: Prediction of network traffic of smart cities based on DE-BP neural network publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2913017 – volume: 72 start-page: 2093 issue: 10–12 year: 2009 ident: 10.1016/j.eswa.2022.118219_b0050 article-title: Modular neural networks with applications to pattern profiling problems publication-title: Neurocomputing doi: 10.1016/j.neucom.2008.10.020 – volume: 9 start-page: 1377 issue: 4 year: 2009 ident: 10.1016/j.eswa.2022.118219_b0130 article-title: Interval type-2 fuzzy logic and modular neural networks for face recognition applications publication-title: Applied Soft Computing Journal doi: 10.1016/j.asoc.2009.06.007 – year: 2019 ident: 10.1016/j.eswa.2022.118219_b0065 – volume: 27 start-page: 402 issue: 2 year: 2016 ident: 10.1016/j.eswa.2022.118219_b0085 article-title: Nonlinear model predictive control based on a self-organizing recurrent neural network publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2015.2465174 – volume: 179 start-page: 2123 issue: 13 year: 2009 ident: 10.1016/j.eswa.2022.118219_b0095 article-title: Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms publication-title: Information Sciences doi: 10.1016/j.ins.2008.07.013 – volume: 197 start-page: 1 year: 2012 ident: 10.1016/j.eswa.2022.118219_b0125 article-title: Genetic optimization of modular neural networks with fuzzy response integration for human recognition publication-title: Information Sciences doi: 10.1016/j.ins.2012.02.027 – volume: 55 start-page: 616 issue: 8 year: 2017 ident: 10.1016/j.eswa.2022.118219_b0035 article-title: Research on traffic flow prediction method based on BP neural network publication-title: Boletin Tecnico/Technical Bulletin – volume: 111 start-page: 1 issue: 1 year: 2013 ident: 10.1016/j.eswa.2022.118219_b0020 article-title: Fuzzy and hard clustering analysis for thyroid disease publication-title: Computer Methods and Programs in Biomedicine doi: 10.1016/j.cmpb.2013.01.002 – volume: 7 start-page: 1113 issue: 8 year: 1998 ident: 10.1016/j.eswa.2022.118219_b0180 article-title: Using a feature-decomposition and data-decomposition modular neural network publication-title: Image (Rochester, N.Y.) – volume: 26 start-page: 417 issue: 3 year: 2015 ident: 10.1016/j.eswa.2022.118219_b0140 article-title: An enhanced fuzzy min-max neural network for pattern classification publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2014.2315214 – volume: 43 start-page: 767 issue: 3 year: 2010 ident: 10.1016/j.eswa.2022.118219_b0060 article-title: Enhanced soft subspace clustering integrating within-cluster and between-cluster information publication-title: Pattern Recognition doi: 10.1016/j.patcog.2009.09.010 – volume: 20 start-page: 631 issue: 5 year: 2007 ident: 10.1016/j.eswa.2022.118219_b0145 article-title: A modular neural network architecture for step-wise learning of grasping tasks publication-title: Neural Networks doi: 10.1016/j.neunet.2007.02.003 – volume: 27 start-page: 416 issue: 2 year: 2016 ident: 10.1016/j.eswa.2022.118219_b0185 article-title: A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2015.2411671 – volume: 125 start-page: 7 year: 2014 ident: 10.1016/j.eswa.2022.118219_b0160 article-title: An online self-adaptive modular neural network for time-varying systems publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.09.038 – ident: 10.1016/j.eswa.2022.118219_b0025 doi: 10.1073/pnas.1510619112 – ident: 10.1016/j.eswa.2022.118219_b0115 doi: 10.1109/TNN.2004.837779 – volume: 23 start-page: 609 issue: 4 year: 2012 ident: 10.1016/j.eswa.2022.118219_b0200 article-title: Fast and efficient second-order method for training radial basis function networks publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2012.2185059 – year: 2020 ident: 10.1016/j.eswa.2022.118219_b0045 article-title: Enhancing network modularity to mitigate catastrophic forgetting publication-title: Applied Network Science doi: 10.1007/s41109-020-00332-9 – volume: 232 start-page: 116 year: 2013 ident: 10.1016/j.eswa.2022.118219_b0190 article-title: Soft large margin clustering publication-title: Information Sciences doi: 10.1016/j.ins.2012.12.040 – year: 2019 ident: 10.1016/j.eswa.2022.118219_b0010 article-title: Reducing catastrophic forgetting in modular neural networks by dynamic information balancing publication-title: ArXiv – volume: 161 year: 2020 ident: 10.1016/j.eswa.2022.118219_b0005 article-title: Deep brain simulation wearable IoT sensor device based Parkinson brain disorder detection using heuristic tubu optimized sequence modular neural network publication-title: Measurement: Journal of the International Measurement Confederation doi: 10.1016/j.measurement.2020.107887 – volume: 7 start-page: 1217 issue: 4 year: 2007 ident: 10.1016/j.eswa.2022.118219_b0120 article-title: A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting publication-title: Applied Soft Computing Journal doi: 10.1016/j.asoc.2006.01.009 – volume: 35 start-page: 229 issue: 1 year: 2002 ident: 10.1016/j.eswa.2022.118219_b0150 article-title: A class-modular feedforward neural network for handwriting recognition publication-title: Pattern Recognition doi: 10.1016/S0031-3203(00)00181-3 – volume: 29 start-page: 766 issue: 4 year: 2018 ident: 10.1016/j.eswa.2022.118219_b0100 article-title: Multicolumn RBF network publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2017.2650865 – volume: 26 start-page: 3123 issue: 12 year: 2015 ident: 10.1016/j.eswa.2022.118219_b0040 article-title: Competition and collaboration in cooperative coevolution of elman recurrent neural networks for time-series prediction publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2015.2404823 |
| SSID | ssj0017007 |
| Score | 2.459938 |
| Snippet | Being a commonly used way for task decomposition in modular neural network (MNN), clustering analysis is employed to decompose the complex task into several... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 118219 |
| SubjectTerms | Improved second-order algorithm Modular neural network RBF neural network Soft subspace clustering |
| Title | Design of a modular neural network based on an improved soft subspace clustering algorithm |
| URI | https://dx.doi.org/10.1016/j.eswa.2022.118219 |
| Volume | 209 |
| WOSCitedRecordID | wos000950627300003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6793 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017007 issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lj9MwELZglwMX3ojlJR-4Rakcu3kdV7AIEFqBWKDiEjmJDam6zqpNl_35zPiRtoBWgMQlqqy6rjyfJuPxN98Q8oyzVrcJgLduBYvhDcFiWadp3CZMKKXhQGB7LH16mx8fF7NZ-c7LE6xsO4HcmOLiojz7r6aGMTA2ls7-hbnHH4UB-AxGhyeYHZ5_ZPgXlpPh6h5P-9bSTFG1EmxhHOc7wldXG1keMpZJLvtzhdlzPUQr8CNwilZRs1ijhIItYVx87Zfd8O10J4uPEsmDF4IOJXJbl-Ej0ceyBZA7uzvyWZl5txl830l3BbQ2WnXbqQhuu6K4YkyXHws1MhtCkks05gAC14tnopybLXIRZ7nrjRj8MLc6Cb_6dJdemE_U6jsKRXE-wUORd7S7WtkfcDFci3OUUuTsKtnneVqCx94_fH00ezNeMOXMVdKHP-frqRz17-eVfh-zbMUhJ7fIDX-AoIfO8LfJFWXukJuhOQf1vvou-eJwQHtNJfU4oA4H1OOAWhzQ3lBpaMABRRzQgAO6wQEdcXCPfHx5dPL8VewbacSNYGyIhWrqEmLzaYYhfauTJuNlphXLmGgaCErbvGBScGxhKzQEkIWE0K1hrE61kIqL-2TP9EY9IHSqNU_rQia6TacCZa6R6MiUShPBZVYfkCRsVdV4lXlsdrKoAp1wXuH2Vri9ldveAxKNc86cxsql306DBSofJbrorwLAXDLv4T_Oe0Sub7D-mOwNy7V6Qq4150O3Wj71uPoBYB6Mjw |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Design+of+a+modular+neural+network+based+on+an+improved+soft+subspace+clustering+algorithm&rft.jtitle=Expert+systems+with+applications&rft.au=Li%2C+Meng&rft.au=Li%2C+Wenjing&rft.au=Qiao%2C+Junfei&rft.date=2022-12-15&rft.pub=Elsevier+Ltd&rft.issn=0957-4174&rft.eissn=1873-6793&rft.volume=209&rft_id=info:doi/10.1016%2Fj.eswa.2022.118219&rft.externalDocID=S0957417422013720 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |