期刊文献+

基于改进资源分配网络的企业能耗单元输入输出模型 被引量:1

Input-output model for enterprise energy-consuming unit based on improved resource allocating networks
下载PDF
导出
摘要 针对企业复杂能耗单元输入输出模型研究的要求,研究了基于改进资源分配网络的能耗单元输入输出模型辨识方法。针对常用的资源分配网络存在的问题,提出了一种基于粗糙集和正交最小二乘的资源分配网络设计方法,通过粗糙集数据分析与处理提取训练样本中典型的数据特征,再结合正交最小二乘学习算法选取对输出能量贡献最大的数据中心加入到隐层节点。实例仿真结果表明,采用该方法辨识企业能耗单元输入输出模型具有结构简单、训练快捷、泛化能力较好等优点。 According to the necessity for researching the input-output model of enterprise complex energy-consuming unit, the modeling method for energy-consuming unit based on improved Resource Allocating Network (RAN) was researched. In view of the existing problems of traditional resource allocating networks, a design method for RAN based on rough set and orthogonal least square (OLS) was proposed. Firstly, rough set was applied to intelligent data analysis for extracting typical characteristics from the training samples, and then OLS was used to select best centers as the hidden layer nodes. The simulation results show that the presented modeling method has the advantages of simple network structure, high convergence rate and better generalization ability, etc.
作者 马福民 王坚
出处 《计算机应用》 CSCD 北大核心 2008年第10期2499-2502,2506,共5页 journal of Computer Applications
基金 国家"十一五"科技支撑计划资助项目(2006BAF01A46) 上海市科技发展基金重点资助项目(061612058) 上海市科委基础研究重点资助项目(06JC14066)
关键词 能耗单元 资源分配网络 粗糙集 正交最小二乘 energy-consuming unit Resource Allocating Network (RAN) rough set orthogonal least square
  • 相关文献

参考文献10

  • 1李丹,余岳峰,虞亚辉.工业企业能效评估方法研究[J].上海节能,2007(5):17-21. 被引量:14
  • 2马福民,王坚.面向企业能效评估的能源消耗过程建模方法研究[J].高技术通讯,2008,18(1):47-53. 被引量:7
  • 3FENG SHU-HU, GUAN XIAO-JI. Energy output prediction model on time series analysis and neural network[ C]//2007 International Conference on Wireless Communications, Networking and Mobile Computing. [S. l. ]: IEEE, 2007:5021 -5024.
  • 4韩敏,穆云峰.一种改进的RAN网络结构优化算法[J].控制与决策,2007,22(10):1177-1180. 被引量:3
  • 5魏海坤,丁维明,宋文忠,徐嗣鑫.RBF网的动态设计方法[J].控制理论与应用,2002,19(5):673-680. 被引量:33
  • 6黎明,张化光.基于粗糙集的神经网络建模方法研究[J].自动化学报,2002,28(1):27-33. 被引量:35
  • 7ZHANG TENG-FEI, WANG XI-HUAI, XIAO JIAN-MEI, et al. RST-based RBF neural network modeling for nonlinear system [ C]// Advances in Neural Networks, LNCS 4491. Berlin: Springer-Verlag, 2007:662-670.
  • 8PAWLAK Z. Rough sets and intelligent data analysis [ J]. Information Sciences, 2002, 147(1) : 1 - 12.
  • 9MICCHELLI C. Interpolation of scattered data: Distance matrices and conditionally positive definite functions[ J]. Constructive Approximation, 1986,2(1) : 11 -22.
  • 10WU DI, ZHANG YA-PING, WANG XIN. RBF neural network for hierarchical intrusion detection systems based on RS feature selection [ J]. Journal of Computational Information Systems, 2007, 3 (4) : 1335 - 1342.

二级参考文献22

  • 1何新贵.模糊Petri网[J].计算机学报,1994,17(12):946-950. 被引量:53
  • 2王珏,苗夺谦,周育健.关于Rough Set理论与应用的综述[J].模式识别与人工智能,1996,9(4):337-344. 被引量:264
  • 3Parekh R, Yang J, Honavar V. Constructive neuralnetwork learning algorithms for pattern classification [J]. IEEE Trans on Neural Networks, 2000, 11 (2) : 436-451.
  • 4Manolis W, Nicolas T, Stefanos K. Intelligent initialization of resource allocating RBF networks [J ]. Neural Networks, 2005, 18(2): 117-122.
  • 5Kyoung M L, Street W N allocating network for An adaptive resource automated detection, segmentation and classification of breast cancer nuclei topic area: Image processing and recognition[J]. IEEE Trans on Neural Networks, 2003, 14(3) : 680-687.
  • 6C-omm J B, Yu D L. Selecting radial basis function network centers with recursive orthogonal least squares training[J]. IEEE Trans on Neural Networks, 2000, 11 (2) : 306-314.
  • 7Theodoridis S. Koutroumbas K. Pattern recognition [M]. Beijing: China Maehine Press. 2003: 552-554.
  • 8贾红安,丁荣华.系统动力学-反馈动态性复杂分析.北京:高等教育出版社,2002.21-36
  • 9Sjoberg J,Zhang Q H,Ljung L,et al.Nonlinear black-box modeling in system identification:a unified overview.Aatomat/ca,1995,31(12):1691-1724
  • 10Sohlberg B.Grey box modelling for model predictive control of a heating process.Journal of Prtwzss Control,2003,13(3):225-238

共引文献87

同被引文献8

  • 1OSTADI B, MOAZZAMI D, REZAIE K. A non-linear programming model for optimization of the electrical energy consumption in typical factory [ J]. Applied Mathematics and Computation, 2007, 187(2) : 944 - 950.
  • 2POLENSKE K R , MCMICHAEL F C . A Chinese cokemaking process-flow model for energy and environmental analyses [ J]. Ener- gy Policy, 2002, 30(10): 865-883.
  • 3YAN Y M, ZHOU J, LIN Y L, et al. Adaptive optimal control mod- el for building cooling and heating sources[ J]. Energy and Build- ings, 2008, 40(8) : 1394 - 1401.
  • 4AZADEH A, GHADERI S F, SOHRABKHANI S. Annual electrici- ty consumption forecasting by neural network in high energy consu- ming industrial sectors [ J]. Energy Conversion and Management, 2008, 49(8) : 2272 -2278.
  • 5KOSANKE K. CIMOSA-overview and status [ J]. Computers in In- dustry, 1995, 27(2) : 101 - 109.
  • 6马福民,王坚.支持企业能效评估的能源消耗过程仿真方法[J].计算机集成制造系统,2008,14(12):2361-2368. 被引量:15
  • 7丛秋梅,柴天佑,余文.污水处理过程的递阶神经网络建模[J].控制理论与应用,2009,26(1):8-14. 被引量:23
  • 8范玉顺,吴澄,王刚,高展.集成化企业建模方法与工具系统研究[J].计算机集成制造系统-CIMS,2000,6(3):1-5. 被引量:44

引证文献1

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部