动态模糊RBF神经网络焊接接头力学性能预测建模
建立动态模糊径向基神经网络RBF（Radial Basis Function,RBF）焊接接头力学性能预测模型,克服静态RBF和模糊神经网络（Fuzzy Neural Network,FNN）在结构辨识、动态样本训练及学习算法的不足。该模型的结构参数不再提前预设,在训练过程中动态自适应调整,适用动态样本数据学习,学习算法引入分级学习和模糊规则修剪策略,加速训练并使模型结构更加紧凑。利用三种厚度、不同工艺TC4钛合金TIG焊接试验数据对该模型进行仿真。结果表明：模型具有较高的预测精度,适用于预测焊接接头力学性能,为焊接过程在线控制开辟了新的途径。...
Saved in:
| Published in | 航空材料学报 Vol. 36; no. 5; pp. 26 - 30 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
内蒙古农业大学 机电工程学院,呼和浩特,010018%内蒙古工业大学 材料科学与工程学院,呼和浩特,010051%内蒙古大学 创业学院商学教学部,呼和浩特,010070
2016
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1005-5053 |
| DOI | 10.11868/j.issn.1005-5053.2016.5.005 |
Cover
| Abstract | 建立动态模糊径向基神经网络RBF（Radial Basis Function,RBF）焊接接头力学性能预测模型,克服静态RBF和模糊神经网络（Fuzzy Neural Network,FNN）在结构辨识、动态样本训练及学习算法的不足。该模型的结构参数不再提前预设,在训练过程中动态自适应调整,适用动态样本数据学习,学习算法引入分级学习和模糊规则修剪策略,加速训练并使模型结构更加紧凑。利用三种厚度、不同工艺TC4钛合金TIG焊接试验数据对该模型进行仿真。结果表明：模型具有较高的预测精度,适用于预测焊接接头力学性能,为焊接过程在线控制开辟了新的途径。 |
|---|---|
| AbstractList | 建立动态模糊径向基神经网络RBF（Radial Basis Function,RBF）焊接接头力学性能预测模型,克服静态RBF和模糊神经网络（Fuzzy Neural Network,FNN）在结构辨识、动态样本训练及学习算法的不足。该模型的结构参数不再提前预设,在训练过程中动态自适应调整,适用动态样本数据学习,学习算法引入分级学习和模糊规则修剪策略,加速训练并使模型结构更加紧凑。利用三种厚度、不同工艺TC4钛合金TIG焊接试验数据对该模型进行仿真。结果表明：模型具有较高的预测精度,适用于预测焊接接头力学性能,为焊接过程在线控制开辟了新的途径。 TG407; 建立动态模糊径向基神经网络RBF( Radial Basis Function,RBF)焊接接头力学性能预测模型,克服静态RBF和模糊神经网络( Fuzzy Neural Network,FNN)在结构辨识、动态样本训练及学习算法的不足。该模型的结构参数不再提前预设,在训练过程中动态自适应调整,适用动态样本数据学习,学习算法引入分级学习和模糊规则修剪策略,加速训练并使模型结构更加紧凑。利用三种厚度、不同工艺TC4钛合金TIG焊接试验数据对该模型进行仿真。结果表明：模型具有较高的预测精度,适用于预测焊接接头力学性能,为焊接过程在线控制开辟了新的途径。 |
| Abstract_FL | A dynamic fuzzy RBF neural network model was built to predict the mechanical properties of welded joints, and the purpose of the model was to overcome the shortcomings of static neural networks including structural identification, dynamic sample training and learning algorithm. The structure and parameters of the model are no longer head of default, dynamic adaptive adjustment in the training, suitable for dynamic sample data for learning, learning algorithm introduces hierarchical learning and fuzzy rule pruning strategy, to accelerate the train-ing speed of model and make the model more compact. Simulation of the model was carried out by using three kinds of thickness and different process TC4 titanium alloy TIG welding test data. The results show that the model has higher prediction accuracy, which is suitable for pre-dicting the mechanical properties of welded joints, and has opened up a new way for the on-line control of the welding process. |
| Author | 张永志 董俊慧 朱红玲 |
| AuthorAffiliation | 内蒙古农业大学机电工程学院,呼和浩特010018 内蒙古工业大学材料科学与工程学院,呼和浩特010051 内蒙古大学创业学院商学教学部,呼和浩特010070 |
| AuthorAffiliation_xml | – name: 内蒙古农业大学 机电工程学院,呼和浩特,010018%内蒙古工业大学 材料科学与工程学院,呼和浩特,010051%内蒙古大学 创业学院商学教学部,呼和浩特,010070 |
| Author_FL | ZHANG Yongzhi DONG Junhui ZHU Hongling |
| Author_FL_xml | – sequence: 1 fullname: ZHANG Yongzhi – sequence: 2 fullname: DONG Junhui – sequence: 3 fullname: ZHU Hongling |
| Author_xml | – sequence: 1 fullname: 张永志 董俊慧 朱红玲 |
| BookMark | eNo9j8FKw0AQhvdQwVr7EiJ4StzJdpPtSbRYFQqC9F52N9k2tW61QdRbiz1FUDwrgmK1F0_qxdLHSaJv4ZaKww_DDN_8w7-EcrqrA4RWAdsAzGXrbTuMIm0DxtSimBLbweDa1DZzDuX_14uoGEWhwBjAKTPK8mgjicdpf5COH7P3-GCrmo0esslNNr3NJvfZME6vR0bJ80cS3yVvL2n_9fty-vM0TD-vksmXuVpGC4p3oqD41wuoXt2uV3at2v7OXmWzZklappb0PIU59T3OSwyEzwLwIOCUUVcp4bhSMUUhCEqOBEYIl8rHpsAnvhKiJEgBrc1tz7hWXDcb7e5pT5uHjdah7JyLWVxMTUxDrsxJ2erq5klo2ONeeMR7Fw3XmwGEYvIL_OBwIg |
| ClassificationCodes | TG407 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2RA 92L CQIGP W92 ~WA 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.11868/j.issn.1005-5053.2016.5.005 |
| DatabaseName | 中文期刊服务平台 中文科技期刊数据库-CALIS站点 维普中文期刊数据库 中文科技期刊数据库-工程技术 中文科技期刊数据库- 镜像站点 Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| DocumentTitleAlternate | Predictive Modeling of Mechanical Properties of Welded Joints Based on Dynamic Fuzzy RBF Neural Network |
| DocumentTitle_FL | Predictive Modeling of Mechanical Properties of Welded Joints Based on Dynamic Fuzzy RBF Neural Network |
| EndPage | 30 |
| ExternalDocumentID | hkclxb201605005 670053350 |
| GrantInformation_xml | – fundername: 国家自然科学基金 funderid: (51165027) |
| GroupedDBID | -03 2B. 2C0 2RA 5VS 5XA 5XC 5XD 92H 92I 92L ACGFS ALMA_UNASSIGNED_HOLDINGS CCEZO CEKLB CQIGP CW9 FIJ GROUPED_DOAJ IPNFZ RIG TCJ TGT U1G U5L U5M W92 ~WA 4A8 93N ABJNI ADMLS PSX |
| ID | FETCH-LOGICAL-c595-c77f0a5d7aa481bd8e171ea5856ffb26cf8f51ee42c1833acfd00001d3dfbb4b3 |
| ISSN | 1005-5053 |
| IngestDate | Thu May 29 04:00:12 EDT 2025 Wed Feb 14 10:14:59 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | 力学性能 welding 预测 焊接 prediction mechanical property dynamic fuzzy RBF neural network 建模 modelling 动态模糊RBF神经网络 |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c595-c77f0a5d7aa481bd8e171ea5856ffb26cf8f51ee42c1833acfd00001d3dfbb4b3 |
| Notes | A dynamic fuzzy RBF neural network model was built to predict the mechanical properties of welded joints, and the purpose of the model was to overcome the shortcomings of static neural networks including structural identification, dynamic sample training and learning algorithm. The structure and parameters of the model are no longer head of default, dynamic adaptive adjustment in the training, suitable for dynamic sample data for learning, learning algorithm introduces hierarchical learning and fuzzy rule pruning strategy, to accelerate the training speed of model and make the model more compact. Simulation of the model was carried out by using three kinds of thickness and different process TC4 titanium alloy TIG welding test data. The results show that the model has higher prediction accuracy, which is suitable for pre-dicting the mechanical properties of welded joints, and has opened up a new way for the on-line control of the welding process. 11-3159/V ZHANG Yongzhi1 , DONG Junhui2, ZHU Hongling3（1. College |
| PageCount | 5 |
| ParticipantIDs | wanfang_journals_hkclxb201605005 chongqing_primary_670053350 |
| PublicationCentury | 2000 |
| PublicationDate | 2016 |
| PublicationDateYYYYMMDD | 2016-01-01 |
| PublicationDate_xml | – year: 2016 text: 2016 |
| PublicationDecade | 2010 |
| PublicationTitle | 航空材料学报 |
| PublicationTitleAlternate | Journal of Aeronautical Materials |
| PublicationTitle_FL | Journal of Aeronautical Materials |
| PublicationYear | 2016 |
| Publisher | 内蒙古农业大学 机电工程学院,呼和浩特,010018%内蒙古工业大学 材料科学与工程学院,呼和浩特,010051%内蒙古大学 创业学院商学教学部,呼和浩特,010070 |
| Publisher_xml | – name: 内蒙古农业大学 机电工程学院,呼和浩特,010018%内蒙古工业大学 材料科学与工程学院,呼和浩特,010051%内蒙古大学 创业学院商学教学部,呼和浩特,010070 |
| SSID | ssib001129858 ssib051375391 ssj0000561693 ssib031741046 ssib023167179 ssib038074666 ssib017479244 |
| Score | 2.0827181 |
| Snippet | 建立动态模糊径向基神经网络RBF（Radial Basis Function,RBF）焊接接头力学性能预测模型,克服静态RBF和模糊神经网络（Fuzzy Neural Network,FNN）在结构辨识、动态样本训练... TG407; 建立动态模糊径向基神经网络RBF( Radial Basis Function,RBF)焊接接头力学性能预测模型,克服静态RBF和模糊神经网络( Fuzzy Neural Network,FNN)在结构辨识、动态样... |
| SourceID | wanfang chongqing |
| SourceType | Aggregation Database Publisher |
| StartPage | 26 |
| SubjectTerms | 力学性能 动态模糊RBF神经网络 建模 焊接 预测 |
| Title | 动态模糊RBF神经网络焊接接头力学性能预测建模 |
| URI | http://lib.cqvip.com/qk/91216X/201605/670053350.html https://d.wanfangdata.com.cn/periodical/hkclxb201605005 |
| Volume | 36 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: Directory of Open Access Journals (DOAJ) issn: 1005-5053 databaseCode: DOA dateStart: 20120101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: true ssIdentifier: ssj0000561693 providerName: Directory of Open Access Journals |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Na9RAFB9KC6IH8RNrVXronGTrJplJZk4y2aYUQQ9SobclmSQtKFs_WpCeKva0guJZERSrvXhSL5b-Obur_4XvTabZaSuuirAsLy8vb977vSF5M5l5IWQGhsZZFKa6UaRR2mBcw31QRnkDcnntB5GXS41TAzdvhQt32I0lvjQ2Me6sWlpfy2b1xi_3lfxLVIEHccVdsn8R2VopMICG-MI_RBj-_yjGNOFUKKoETUJcsiA8JOBQARHROICzt-N5pBWnMjHMmArDieeo9CxHtpAQDLWhqgTlhwSnitGY2eZkbDhzVIW2XRXRRFARoM5EUuWjKjgVg7wRhiZiVdvmJsTmQmAqY6S0YhJsaxoCfvJgc-Av3-8mRneLKiMbN2ksDGeeymgoItBPFdCE4RnrIWh0REJEIK7QUGg_opHQ2HcnRardmqYDGySMFtTuWxsBV8XsKUSUoT1SWfwQJOvH1f0WjdeSIVBotwkT4iAsbtZriQ2AVX4LmeBNXBGAc8viDNChzQCgpOZT6k1PUJ-PMNS2OMLQQ8GIUAK7Tm2fuR7w-u8-cG-0D0cs5tihsJO6bh00Ahs2St2eBZwDfU1ijwY3_sri6vs09jGP9Xch9w_cPKAqxGPvd9x9qIdOeli9RTyaeIhQmMwD9c_W-nHtaDiL07Z8mHDVy2BX7up7jzMUaXJTiHnCx1lFZ2LIDGogJXcqTvlYu8IbvluHAQDDhRr1MZa3ciYluBdEPLAVsaovHoRYgcksA7FmHiMz-05c-40LWH9mZbWz_ACyarPJsVOmnWUnH188RU7agfS0qu6Kp8nYxsoZcsIpr3qWXO91d_qbT_o7bwefu3AfHGy_Gey-GOy9HOy-Hmx1-8-34dd7_6XXfdX79KG_-fH7070f77b6X5_1dr_BVefI4nyy2Fpo2A_GNDSXvKGjqGymPI_SlMFoPBeFF3lFygUPyzLzQ12KkntFwXwNiUyQ6jI3LzfzIC-zjGXBeTLeWe0UF8g0K2Hkp0suPT_DvfeCaciEdCZAE08zPkmmaiTa96u6QG3c8QijZ96cJNMWm7Z9WjxqH4r1xdEiU-Q40tV87yUyvvZwvbgMI6C17IrpID8B7nnzBQ |
| linkProvider | Directory of Open Access Journals |
| 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=%E5%8A%A8%E6%80%81%E6%A8%A1%E7%B3%8ARBF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%84%8A%E6%8E%A5%E6%8E%A5%E5%A4%B4%E5%8A%9B%E5%AD%A6%E6%80%A7%E8%83%BD%E9%A2%84%E6%B5%8B%E5%BB%BA%E6%A8%A1&rft.jtitle=%E8%88%AA%E7%A9%BA%E6%9D%90%E6%96%99%E5%AD%A6%E6%8A%A5&rft.au=%E5%BC%A0%E6%B0%B8%E5%BF%97&rft.au=%E8%91%A3%E4%BF%8A%E6%85%A7&rft.au=%E6%9C%B1%E7%BA%A2%E7%8E%B2&rft.date=2016&rft.pub=%E5%86%85%E8%92%99%E5%8F%A4%E5%86%9C%E4%B8%9A%E5%A4%A7%E5%AD%A6+%E6%9C%BA%E7%94%B5%E5%B7%A5%E7%A8%8B%E5%AD%A6%E9%99%A2%2C%E5%91%BC%E5%92%8C%E6%B5%A9%E7%89%B9%2C010018%25%E5%86%85%E8%92%99%E5%8F%A4%E5%B7%A5%E4%B8%9A%E5%A4%A7%E5%AD%A6+%E6%9D%90%E6%96%99%E7%A7%91%E5%AD%A6%E4%B8%8E%E5%B7%A5%E7%A8%8B%E5%AD%A6%E9%99%A2%2C%E5%91%BC%E5%92%8C%E6%B5%A9%E7%89%B9%2C010051%25%E5%86%85%E8%92%99%E5%8F%A4%E5%A4%A7%E5%AD%A6+%E5%88%9B%E4%B8%9A%E5%AD%A6%E9%99%A2%E5%95%86%E5%AD%A6%E6%95%99%E5%AD%A6%E9%83%A8%2C%E5%91%BC%E5%92%8C%E6%B5%A9%E7%89%B9%2C010070&rft.issn=1005-5053&rft.volume=36&rft.issue=5&rft.spage=26&rft.epage=30&rft_id=info:doi/10.11868%2Fj.issn.1005-5053.2016.5.005&rft.externalDocID=hkclxb201605005 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F91216X%2F91216X.jpg http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fhkclxb%2Fhkclxb.jpg |