山东省花生年产量的组合预测模型研究
以山东省花生年产量为研究对象.针对花生年产量的强烈波动性而导致的预测难、准确率低等难题,提出了一种基于GM（1,1）和RBF神经网络的组合预测模型,利用GM（1,1）来捕捉花生年产量的总体趋势,RBF神经网络来预测带有强烈非线性的残差项;同时为了提高RBF神经网络的训练速度和精度,针对标准遗传算法存在的早熟现象和收敛速度慢的缺点,提出了一种改进的自适应遗传算法,对RBF神经网络的初始参数进行优化.试验结果表明,组合预测模型可以较准确预测花生年产量,说明了组合预测模型的可行性....
Saved in:
| Published in | 广东农业科学 Vol. 41; no. 21; pp. 11 - 15 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
东北农业大学经济管理学院,黑龙江哈尔滨,150030
2014
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1004-874X |
Cover
| Abstract | 以山东省花生年产量为研究对象.针对花生年产量的强烈波动性而导致的预测难、准确率低等难题,提出了一种基于GM（1,1）和RBF神经网络的组合预测模型,利用GM（1,1）来捕捉花生年产量的总体趋势,RBF神经网络来预测带有强烈非线性的残差项;同时为了提高RBF神经网络的训练速度和精度,针对标准遗传算法存在的早熟现象和收敛速度慢的缺点,提出了一种改进的自适应遗传算法,对RBF神经网络的初始参数进行优化.试验结果表明,组合预测模型可以较准确预测花生年产量,说明了组合预测模型的可行性. |
|---|---|
| AbstractList | 以山东省花生年产量为研究对象.针对花生年产量的强烈波动性而导致的预测难、准确率低等难题,提出了一种基于GM（1,1）和RBF神经网络的组合预测模型,利用GM（1,1）来捕捉花生年产量的总体趋势,RBF神经网络来预测带有强烈非线性的残差项;同时为了提高RBF神经网络的训练速度和精度,针对标准遗传算法存在的早熟现象和收敛速度慢的缺点,提出了一种改进的自适应遗传算法,对RBF神经网络的初始参数进行优化.试验结果表明,组合预测模型可以较准确预测花生年产量,说明了组合预测模型的可行性. S565.2%F326.12; 以山东省花生年产量为研究对象.针对花生年产量的强烈波动性而导致的预测难、准确率低等难题,提出了一种基于GM(1,1)和RBF神经网络的组合预测模型,利用GM(1,1)来捕捉花生年产量的总体趋势,RBF神经网络来预测带有强烈非线性的残差项;同时为了提高RBF神经网络的训练速度和精度,针对标准遗传算法存在的早熟现象和收敛速度慢的缺点,提出了一种改进的自适应遗传算法,对RBF神经网络的初始参数进行优化.试验结果表明,组合预测模型可以较准确预测花生年产量,说明了组合预测模型的可行性. |
| Author | 张永强 才正 王刚毅 |
| AuthorAffiliation | 东北农业大学经济管理学院,黑龙江哈尔滨150030 |
| AuthorAffiliation_xml | – name: 东北农业大学经济管理学院,黑龙江哈尔滨,150030 |
| Author_FL | ZHANG Yong-qiang WANG Gang-yi CAI Zheng |
| Author_FL_xml | – sequence: 1 fullname: ZHANG Yong-qiang – sequence: 2 fullname: CAI Zheng – sequence: 3 fullname: WANG Gang-yi |
| Author_xml | – sequence: 1 fullname: 张永强 才正 王刚毅 |
| BookMark | eNrjYmDJy89LZWHgNDQwMNG1MDeJ4GDgLS7OTDIwMDQ0MDQyMORkMH66ceOTHXOez2l80bXx-ZT5T3duebJr-cv2_uezWp7vbnk6oePlopZnW7ufrVj4dF738wVTnq_cxsPAmpaYU5zKC6W5GULcXEOcPXR9_N09nR19dJNNLY11LY0Nk8wtk0zTEs1NjYxNTExSzRPNLS0tTZNMLUxTDE1Tk0yTzdKMDFINgWKGyaYpyUnJ5ilJhpaGpmZplqlpxtwM6hBjyxPz0hLz0uOz8kuL8oAWxqen5FVmVwDdb2IE9JoxUKUSRGVyRn5eemEmUG1BUWZuYlFlvJmZsZGFJVCdMQCGNF-h |
| ClassificationCodes | S565.2%F326.12 |
| 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 W95 ~WA 2B. 4A8 92I 93N PSX TCJ |
| DatabaseName | 维普期刊资源整合服务平台 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-农业科学 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 |
| DocumentTitleAlternate | Combined predictive model research of annual production of peanut in Shandong province |
| DocumentTitle_FL | Combined predictive model research of annual production of peanut in Shandong province |
| EndPage | 15 |
| ExternalDocumentID | gdnykx201421003 663289421 |
| GrantInformation_xml | – fundername: 黑龙江省自然科学基金; 国家自然科学基金; 中国博士后科学基金; 黑龙江省博士后科学基金 funderid: (G201112); (71173035); (20100480973); (LBH-Z10211) |
| GroupedDBID | -04 2B. 2B~ 2RA 5XA 5XE 92G 92I 92L ABDBF ACGFS ALMA_UNASSIGNED_HOLDINGS CCEZO CHDYS CQIGP CW9 GROUPED_DOAJ TCJ TGD U1G U5N W95 ~WA 4A8 93N ABJNI PSX |
| ID | FETCH-LOGICAL-c593-931b79b5fa7523444e7a79995b585d15eb5c6f20e19951c5dcbc7db19156f9ef3 |
| ISSN | 1004-874X |
| IngestDate | Thu May 29 03:55:15 EDT 2025 Wed Feb 14 10:35:50 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 21 |
| Keywords | 花生产量预测 RBF神经网络 遗传算法 组合模型 |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c593-931b79b5fa7523444e7a79995b585d15eb5c6f20e19951c5dcbc7db19156f9ef3 |
| Notes | 44-1267/S peanut annual production prediction; RBF neural network; genetic algorithm; combined model ZHANG Yong-qiang, CAI Zheng, WANG Gang-yi （College of Economics and Management, Northeast Agricultural University,Harbin 150030, China） This paper studies annual production of peanut in Shandong province. Considering the problem of difficult prediction and low accuracy due to strong volatility in peanut annual production, this paper proposes a novel combined model on the basis of GM （13） model and RBF neural network. GM （1,1） is to capture the global trend of peanut annual production, and RBF neural network is to predict the strong nonlinear residual item. To improve the training velocity and accuracy, considering the precocious phenomenon and slow convergence rate of standard genetic algorithm, a new self- adaptive genetic algorithm is proposed to optimize initial parameters of RBF neural network. Experimental results demonstrate the new combined model can accurately predict the peanut annual production, which s |
| PageCount | 5 |
| ParticipantIDs | wanfang_journals_gdnykx201421003 chongqing_primary_663289421 |
| PublicationCentury | 2000 |
| PublicationDate | 2014 |
| PublicationDateYYYYMMDD | 2014-01-01 |
| PublicationDate_xml | – year: 2014 text: 2014 |
| PublicationDecade | 2010 |
| PublicationTitle | 广东农业科学 |
| PublicationTitleAlternate | Guangdong Agricultural Sciences |
| PublicationTitle_FL | Guangdong Agricultural Sciences |
| PublicationYear | 2014 |
| Publisher | 东北农业大学经济管理学院,黑龙江哈尔滨,150030 |
| Publisher_xml | – name: 东北农业大学经济管理学院,黑龙江哈尔滨,150030 |
| SSID | ssib001101201 ssib017478013 ssib051371967 ssj0000993252 |
| Score | 1.9427781 |
| Snippet | ... S565.2%F326.12;... |
| SourceID | wanfang chongqing |
| SourceType | Aggregation Database Publisher |
| StartPage | 11 |
| SubjectTerms | RBF神经网络 组合模型 花生产量预测 遗传算法 |
| Title | 山东省花生年产量的组合预测模型研究 |
| URI | http://lib.cqvip.com/qk/93806X/201421/663289421.html https://d.wanfangdata.com.cn/periodical/gdnykx201421003 |
| Volume | 41 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1Ni9QwtKx78iKKiuuqzMEcR5omaZJj221ZBD2NsLdh0mlnQej6MQu6N2FAcQ962JMsfl3U44CKH39nZ8f9F76XdGaqLPgBQ3i8vCTvY9L38pqknncVgu6gZ4KwDd7ctHnow5RSlLU5L_0-y42QFE8j37gZrt_i1zfExtIJ3di1tD001_KdY8-V_I9VAQd2xVOy_2DZeaeAABjsCyVYGMq_sjFJBYkp_lJOYkV0QlKJpQKMIiqyVYDhRGeWWJOYW-KIRJKkmihJVGZpIqI4AnFsAUG0T5RCmiiwmJDEgqgYgUiRiFqa1GIkiXwcBQEYImyGvLNxswaT0E9oAYeJbEPgwfYZrZFoni-0rRPsHsf3kdxhFgkGrFEgyZrlDBqzRQ1I5zgUKAsOBCQZcd_vmWU76CLP-SuPCdHyOGaBR271VzM7U1s2U1JqBUqtkqRVZNgg1kRbpQYJwtAQ5QYgRTz2QGtzaWYtIFBw7SwQo-6DBAJsv37T5DwK7vRR0m1Dnbkcd9dXPbXcCfHagdSep6i9ycJLz_dODvrVw9sPUDewWrf33EKMqRrJBBsI47Vt_vzJLSiT8KiV8-wjrAtYINxWgJo_vFxkc6sa3IWQyZ5gq8peNWgEW53T3ql6ldSK3F_-jLe0s3nWY5Px-ODL_nT_0Y-n4-neq8nXjwff3h09fjZ9MZp-H02ePzl6Ozr8tHv4_s3k5e709d70w-dzXidLO8l6u_7mRzsXmrU1o0ZqI8qeFAHjnBeyJ2ENIwwsa_tUFEbkYRn4Bd4sQHPRz00u-4ZqKsJSFyU77y1XW1VxwWv5rB_wsCxLymGVgIQcEyAlVcbwgrMVb3Uub_eOu9qlC_F3oDRodsVr1Rro1hP-fvc3zV_8M8mqdxJhl7K75C0P720XlyGIHZor1l4_AeQzcfQ |
| 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%B1%B1%E4%B8%9C%E7%9C%81%E8%8A%B1%E7%94%9F%E5%B9%B4%E4%BA%A7%E9%87%8F%E7%9A%84%E7%BB%84%E5%90%88%E9%A2%84%E6%B5%8B%E6%A8%A1%E5%9E%8B%E7%A0%94%E7%A9%B6&rft.jtitle=%E5%B9%BF%E4%B8%9C%E5%86%9C%E4%B8%9A%E7%A7%91%E5%AD%A6&rft.au=%E5%BC%A0%E6%B0%B8%E5%BC%BA&rft.au=%E6%89%8D%E6%AD%A3&rft.au=%E7%8E%8B%E5%88%9A%E6%AF%85&rft.date=2014&rft.pub=%E4%B8%9C%E5%8C%97%E5%86%9C%E4%B8%9A%E5%A4%A7%E5%AD%A6%E7%BB%8F%E6%B5%8E%E7%AE%A1%E7%90%86%E5%AD%A6%E9%99%A2%2C%E9%BB%91%E9%BE%99%E6%B1%9F%E5%93%88%E5%B0%94%E6%BB%A8%2C150030&rft.issn=1004-874X&rft.volume=41&rft.issue=21&rft.spage=11&rft.epage=21&rft.externalDocID=gdnykx201421003 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F93806X%2F93806X.jpg http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fgdnykx%2Fgdnykx.jpg |