Specifications

531.1 Metallurgy - 422.2 Strength of Building Materials : Test Methods - 422 Strength of
Building Materials; Test Equipment and Methods - 421 Strength of Building Materials;
Mechanical Properties - 531.2 Metallography
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
© 2013 Elsevier Inc. All rights reserved.
20130706 新增 12 条
1.
Accession number: 20132616443185
Title: Reservoir systems operation model using simulation and neural network
Authors: Chang, Jianxia1, 2 ; Wang, Yimin1 ; Huang, Qiang1/畅建霞;王义民;黄强
Author affiliation:
1 Xi 'An University of Technology, Shaan xi, Xi' an, 710048, China
2 Xi'An University of Architecture and Technology, Shaan xi, Xi' an, 710048, China
Source title: Artificial Intelligence Applications and Innovations - IFIP TC12 WG12.5 - 2nd
IFIP Conference on Artificial Intelligence Applications and Innovations, AIAI 2005
Abbreviated source title: IFIP TC WG - IFIP Conf. Artif. Intell. Appl. Innovations, AIAI
Monograph title: Artificial Intelligence Applications and Innovations - IFIP TC12 WG12.5 -
2nd IFIP Conference on Artificial Intelligence Applications and Innovations, AIAI 2005
Issue date: 2005
Publication year: 2005
Pages: 519-526
Language: English
ISBN-10: 0387283188
ISBN-13: 9780387283180
Document type: Conference article (CA)
Conference name: 2nd International Conference on Artificial Intelligence Applications and
Innovations, AIAI 2005
Conference date: September 7, 2005 - September 9, 2005
Conference location: Beijing, China
Conference code: 97354
Sponsor: IFIP Tech. Comm. Artif. Intell. (Tech. Comm.); Working Group 12.5 (Artificial
Intelligence Applications)
Publisher: Springer Science and Business Media, LLC, 233 Spring Street, New York, NY
10013, United States
Abstract: For multi-reservoir operating rules, a simulation-based neural network model is
developed in this study. In the suggested model, multi-reservoir operating rules are derived using
a neural network from the results of simulation. The training of the neural network is done using
a supervised learning approach with the back propagation algorithm. The Yellow River upstream
multi reservoir system is used for this study. This paper presents the usefulness of the neural
network in deriving general operating policies for a multi-reservoir system.