Artificial Intelligence in Engineering最新文献_第9页

Automatic design synthesis with artificial intelligence techniques 采用人工智能技术的自动设计综合

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(99)00014-X

F.J Vico , F.J Veredas , J.M Bravo , J Almaraz

引用次数: 30

A knowledge based database system for engineering correlations 基于知识的工程关联数据库系统

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(99)00015-1

M.A. Moss , K. Jambunathan , E. Lai

{"title":"A knowledge based database system for engineering correlations","authors":"M.A. Moss , K. Jambunathan , E. Lai","doi":"10.1016/S0954-1810(99)00015-1","DOIUrl":"10.1016/S0954-1810(99)00015-1","url":null,"abstract":"<div><p>Engineering design frequently relies on empirical data expressed in the form of non-dimensional correlations. These are almost always governed by applicability limits and the engineer is faced with the problem of choosing the right correlation that would provide design data with acceptable accuracy from a large number which are available. A knowledge based database system (KBDS) has been constructed which assists in the simple formulation of a jet impingement application based on which it retrieves and evaluates the relevant correlation from a database. Where the information in the database does not satisfy this specification the system uses knowledge of the application domain to either select suitable correlations for extrapolation or to modify the database query to select alternative information. The constraints which enable new correlations to be added or the knowledge in the network to be extended to include new geometries and flow conditions whilst maintaining the integrity are described. The operation of the KBDS has been demonstrated with a comprehensive database of correlations for the heat transfer due to the impingement of single and multiple air jets. This application provides typical engineering correlations and hence the techniques described are expected to be widely applicable.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":"13 3","pages":"Pages 201-210"},"PeriodicalIF":0.0,"publicationDate":"1999-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(99)00015-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83988980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Rule-base content verification using a digraph-based modelling approach 使用基于有向图的建模方法进行基于规则的内容验证

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(99)00013-8

G.S. Gursaran , S. Kanungo , A.K. Sinha

引用次数: 11

Combined analytical and empirical learning framework for branch and bound algorithms: the knapsack problem 分支定界算法的综合分析与经验学习框架:背包问题

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(99)00004-7

M.J. Realff , P.H. Kvam , W.E. Taylor

{"title":"Combined analytical and empirical learning framework for branch and bound algorithms: the knapsack problem","authors":"M.J. Realff , P.H. Kvam , W.E. Taylor","doi":"10.1016/S0954-1810(99)00004-7","DOIUrl":"10.1016/S0954-1810(99)00004-7","url":null,"abstract":"<div><p>Optimization methods are being applied to engineering problem solving with increasing frequency as computer hardware and software improves. The configuration of an optimization algorithm can make a significant difference to the efficiency of the solution process. This article examines the use of one such optimization strategy, branch and bound, for the solution of the classic knapsack problem. It is shown that the best configuration of the algorithm can be data dependent and hence that an ‘intelligent’ optimization system will need to automatically configure itself with the control knowledge appropriate to the problems the user is solving. A two-step approach is taken to configuring the algorithm. First, an analytical learning method, explanation based learning is used to derive a provably correct dominance condition for the knapsack problem. Second, the algorithm is configured with and without the condition, and subjected to a rigorous statistical test of performance, on the user's data, to decide which configuration is the best.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":"13 3","pages":"Pages 287-300"},"PeriodicalIF":0.0,"publicationDate":"1999-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(99)00004-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77873518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Fast training algorithm for feedforward neural networks: application to crowd estimation at underground stations 前馈神经网络快速训练算法在地铁站人群估计中的应用

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(99)00016-3

T.W.S. Chow, J.Y.-F. Yam, S.-Y Cho

引用次数: 27

Genetic algorithms for designing multihop lightwave network topologies 多跳光波网络拓扑设计的遗传算法

Artificial Intelligence in Engineering Pub Date : 1999-07-01 DOI: 10.1016/S0954-1810(98)00019-3

C. Gazen, C. Ersoy

{"title":"Genetic algorithms for designing multihop lightwave network topologies","authors":"C. Gazen, C. Ersoy","doi":"10.1016/S0954-1810(98)00019-3","DOIUrl":"10.1016/S0954-1810(98)00019-3","url":null,"abstract":"<div><p>Multihop lightwave networks are a means of utilizing the large bandwidth of optical fibers. In these networks, each node has a fixed number of transmitters and receivers connected to a common optical medium. A multihop topology is implemented logically by assigning different wavelengths to pairs of transmitters and receivers. By using tunable lasers or receivers, it is possible to modify the topology dynamically when node failures occur or traffic loads change. The reconfigurability of logical multihop lightwave networks requires that optimal topologies and flow assignments be found. In this article, optimization of these logical topologies by genetic algorithms is investigated. The genetic algorithm takes topologies as individuals of its population, and tries to find optimal ones by mating, mutating and eliminating them. During the evolution of solutions, minimum hop routing with flow deviation is used to assign flows, and evaluate the fitness of topologies. The algorithm is tested with different sets of parameters and types of traffic matrices and the solutions are compared against histograms of random samples from the solution space. These tests show that the solutions found by the genetic algorithm are comparable with and in some cases better than those found by existing heuristic algorithms.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":"13 3","pages":"Pages 211-221"},"PeriodicalIF":0.0,"publicationDate":"1999-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(98)00019-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79016546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

Artificial neural networks as applied to long-term demand forecasting 人工神经网络在长期需求预测中的应用

Artificial Intelligence in Engineering Pub Date : 1999-04-01 DOI: 10.1016/S0954-1810(98)00018-1

Tawfiq Al-Saba, Ibrahim El-Amin

引用次数: 165

Adaptive navigation of autonomous vehicles using evolutionary algorithms 基于进化算法的自动驾驶汽车自适应导航

Artificial Intelligence in Engineering Pub Date : 1999-04-01 DOI: 10.1016/S0954-1810(98)00012-0

Andreas C Nearchou

引用次数: 45

Training Elman and Jordan networks for system identification using genetic algorithms 使用遗传算法训练Elman和Jordan网络进行系统识别

Artificial Intelligence in Engineering Pub Date : 1999-04-01 DOI: 10.1016/S0954-1810(98)00013-2

D.T Pham, D Karaboga

引用次数: 88

Self-tuning fuzzy controller design using genetic optimisation and neural network modelling 基于遗传优化和神经网络建模的自整定模糊控制器设计

Artificial Intelligence in Engineering Pub Date : 1999-04-01 DOI: 10.1016/S0954-1810(98)00017-X

D.T. Pham, D. Karaboga

引用次数: 26