Artificial Intelligence in Engineering最新文献_第10页

Model switching in intelligent control systems 智能控制系统中的模型切换

Artificial Intelligence in Engineering Pub Date : 1999-04-01 DOI: 10.1016/S0954-1810(98)00016-8

Mohan Ravindranathan, Roy Leitch

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引用次数: 17

Autonomous agent based on reinforcement learning and adaptive shadowed network 基于强化学习和自适应阴影网络的自主智能体

Artificial Intelligence in Engineering Pub Date : 1999-04-01 DOI: 10.1016/S0954-1810(98)00020-X

Bojan Jerbć, Katarina Grolinger, Božo Vranjš

{"title":"Autonomous agent based on reinforcement learning and adaptive shadowed network","authors":"Bojan Jerbć, Katarina Grolinger, Božo Vranjš","doi":"10.1016/S0954-1810(98)00020-X","DOIUrl":"10.1016/S0954-1810(98)00020-X","url":null,"abstract":"<div><p>The planning of intelligent robot behavior plays an important role in the development of flexible automated systems. The robot’s intelligence comprises its capability to act in unpredictable and chaotic situations, which requires not just a change but the creation of the robot’s working knowledge. Planning of intelligent robot behavior addresses three main issues: finding task solutions in unknown situations, learning from experience and recognizing the similarity of problem paradigms. This article outlines a planning system which integrates the reinforcement learning method and a neural network approach with the aim to ensure autonomous robot behavior in unpredictable working conditions.</p><p>The assumption is that the robot is a tabula rasa and has no knowledge of the work space structure. Initially, it has just basic strategic knowledge of searching for solutions, based on random attempts, and a built-in learning system. The reinforcement learning method is used here to evaluate robot behavior and to induce new, or improve the existing, knowledge. The acquired action (task) plan is stored as experience which can be used in solving similar future problems. To provide the recognition of problem similarities, the Adaptive Fuzzy Shadowed neural network is designed. This novel network concept with a fuzzy learning rule and shadowed hidden layer architecture enables the recognition of slightly translated or rotated patterns and does not forget already learned structures.</p><p>The intelligent planning system is simulated using object-oriented techniques and verified on planned and random examples, proving the main advantages of the proposed approach: autonomous learning, which is invariant with regard to the order of training samples, and single iteration learning progress.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(98)00020-X","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90247308","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: 19

An extended Kalman filter and neural network cascade fault diagnosis strategy for the glutamic acid fermentation process 谷氨酸发酵过程的扩展卡尔曼滤波和神经网络级联故障诊断策略

Artificial Intelligence in Engineering Pub Date : 1999-04-01 DOI: 10.1016/S0954-1810(98)00007-7

Wei Liu

引用次数: 33

The structure of a physical behaviour description facility 物理行为描述设备的结构

Artificial Intelligence in Engineering Pub Date : 1999-01-01 DOI: 10.1016/S0954-1810(98)00002-8

S. Chandra

引用次数: 2

A verification method for systolic arrays using induction-based theorem provers 用基于归纳的定理证明器验证收缩阵列的方法

Artificial Intelligence in Engineering Pub Date : 1999-01-01 DOI: 10.1016/S0954-1810(98)00010-7

Kazuko Takahashi , Hiroshi Fujita

引用次数: 0

A toolset for construction of hybrid intelligent forecasting systems: application for water demand prediction 构建混合智能预测系统的工具集:在需水量预测中的应用

Artificial Intelligence in Engineering Pub Date : 1999-01-01 DOI: 10.1016/S0954-1810(98)00008-9

Narate Lertpalangsunti , Christine W. Chan , Ralph Mason , Paitoon Tontiwachwuthikul

{"title":"A toolset for construction of hybrid intelligent forecasting systems: application for water demand prediction","authors":"Narate Lertpalangsunti , Christine W. Chan , Ralph Mason , Paitoon Tontiwachwuthikul","doi":"10.1016/S0954-1810(98)00008-9","DOIUrl":"10.1016/S0954-1810(98)00008-9","url":null,"abstract":"<div><p>This paper presents the Intelligent Forecasters Construction Set (IFCS) which is a toolset for constructing forecasting applications. The toolset supports the intelligent techniques of fuzzy logic, artificial neural networks, knowledge-based and case-based reasoning. The developer can construct a forecasting application using rules, procedures and flow diagrams, which are organized into a hierarchy of workspaces. The modularity of the IFCS allows subsequent addition of other modules of intelligent techniques.</p><p>The IFCS was used for developing a water demand forecasting system based on real-world data obtained from the City of Regina's water distribution system and Environment Canada. A utility demand prediction system developed with the IFCS system is useful for optimizing operation costs of water plants. Some water plants need to pay a flat rate for electricity, which is set depending on peak kilowatt demand. Hence, if the peak kilowatt demand can be reduced, the operating costs of the plant can be lessened (Jamieson RA et al. American Water Works Association Journal 1993;85:48–55). An energy management system needs a good estimate of future customer demand in order to find the optimal pumping schedules that can minimize the peak kilowatt demand. Since the IFCS supports developing multiple predictor models, modeling of data can be expedited. The benefits of using multiple modules of artificial neural networks for demand prediction are presented. The results from this approach are compared with a linear regression and a case-based reasoning program. The performance comparisons among the forecasters will be discussed.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(98)00008-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75087200","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: 49

Review of the applications of neural networks in chemical process control — simulation and online implementation 神经网络在化工过程控制中的应用综述——仿真与在线实现

Artificial Intelligence in Engineering Pub Date : 1999-01-01 DOI: 10.1016/S0954-1810(98)00011-9

Mohamed Azlan Hussain

{"title":"Review of the applications of neural networks in chemical process control — simulation and online implementation","authors":"Mohamed Azlan Hussain","doi":"10.1016/S0954-1810(98)00011-9","DOIUrl":"10.1016/S0954-1810(98)00011-9","url":null,"abstract":"<div><p>As a result of good modeling capabilities, neural networks have been used extensively for a number of chemical engineering applications such as sensor data analysis, fault detection and nonlinear process identification. However, only in recent years, with the upsurge in the research on nonlinear control, has its use in process control been widespread. This paper intend to provide an extensive review of the various applications utilizing neural networks for chemical process control, both in simulation and online implementation. We have categorized the review under three major control schemes; predictive control, inverse-model-based control, and adaptive control methods, respectively. In each of these categories, we summarize the major applications as well as the objectives and results of the work. The review reveals the tremendous prospect of using neural networks in process control. It also shows the multilayered neural network as the most popular network for such process control applications and also shows the lack of actual successful online applications at the present time.</p></div>","PeriodicalId":100123,"journal":{"name":"Artificial Intelligence in Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1999-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0954-1810(98)00011-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85272445","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: 399

A factorisation model of robotic tasks 机器人任务的分解模型

Artificial Intelligence in Engineering Pub Date : 1999-01-01 DOI: 10.1016/S0954-1810(98)00001-6

Salima Benbernou

引用次数: 1

A new method for diagnostic problem solving based on a fuzzy abductive inference model and the tabu search approach 基于模糊溯因推理模型和禁忌搜索的诊断问题求解新方法

Artificial Intelligence in Engineering Pub Date : 1999-01-01 DOI: 10.1016/S0954-1810(98)00004-1

F. Wen, C.S. Chang

引用次数: 7

Representing user preference in engineering design domains using an enhanced weighted fuzzy reasoning algorithm 用增强加权模糊推理算法表示工程设计领域的用户偏好

Artificial Intelligence in Engineering Pub Date : 1999-01-01 DOI: 10.1016/S0954-1810(97)10005-X

Christine W. Chan, Patrick Lau

引用次数: 4