標(biāo)題: Titlebook: Automatic Learning Techniques in Power Systems; Louis A. Wehenkel Book 1998 Springer Science+Business Media New York 1998 Counter.Soft Com [打印本頁(yè)] 作者: Croching 時(shí)間: 2025-3-21 16:53
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書(shū)目名稱(chēng)Automatic Learning Techniques in Power Systems讀者反饋
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作者: –FER 時(shí)間: 2025-3-21 22:55 作者: Expand 時(shí)間: 2025-3-22 03:00 作者: debble 時(shí)間: 2025-3-22 06:13
A. C. Pic,V. A. Moore,N. A. Burnhamnetwork approaches becomes less and less relevant. This will become more obvious in the course of this and the next chapter. One of the common characteristics of these methods is that they handle input information in the form of numerical attributes. Thus all non-numerical information must be transl作者: 他一致 時(shí)間: 2025-3-22 08:44 作者: Popcorn 時(shí)間: 2025-3-22 14:44 作者: Left-Atrium 時(shí)間: 2025-3-22 20:07 作者: 玷污 時(shí)間: 2025-3-22 23:11 作者: 少量 時(shí)間: 2025-3-23 02:18
https://doi.org/10.1057/9781137388254. We start by describing probabilistic security assessment and its connection with the automatic learning framework. Then we discuss the need for enhancing automatic learning methods in order to exploit more efficiently temporal data. We conclude by proposing a software architecture which should be 作者: Stable-Angina 時(shí)間: 2025-3-23 09:31
Automatic Learning Techniques in Power Systems978-1-4615-5451-6Series ISSN 2196-3185 Series E-ISSN 2196-3193 作者: Consensus 時(shí)間: 2025-3-23 10:47 作者: 精密 時(shí)間: 2025-3-23 16:30
978-1-4613-7489-3Springer Science+Business Media New York 1998作者: 雪白 時(shí)間: 2025-3-23 20:44
Nanotechnology Safety EducationWhile the learning systems based on artificial neural networks became popular only in the early eighties, they have a much longer research history and some of these methods have evolved towards quite mature techniques.作者: Cpap155 時(shí)間: 2025-3-23 23:13
Youth Civic Engagement: A Global PerspectiveIn the preceding three chapters we have described main automatic learning methods from three different paradigms : classical statistics, artificial neural nets and machine learning.作者: lethal 時(shí)間: 2025-3-24 03:34
Yunqi Wang,Rosario Esteinou,Yan Ruth XiaIn this chapter we provide a brief overview of power system security and possible applications of automatic learning.作者: Adornment 時(shí)間: 2025-3-24 08:18 作者: 疏遠(yuǎn)天際 時(shí)間: 2025-3-24 13:10 作者: 打火石 時(shí)間: 2025-3-24 17:21
Artificial Neural NetworksWhile the learning systems based on artificial neural networks became popular only in the early eighties, they have a much longer research history and some of these methods have evolved towards quite mature techniques.作者: 異端 時(shí)間: 2025-3-24 20:40
Auxiliary Tools and Hybrid TechniquesIn the preceding three chapters we have described main automatic learning methods from three different paradigms : classical statistics, artificial neural nets and machine learning.作者: 狂亂 時(shí)間: 2025-3-24 23:42
Overview of Security ProblemsIn this chapter we provide a brief overview of power system security and possible applications of automatic learning.作者: 爭(zhēng)吵 時(shí)間: 2025-3-25 03:32 作者: frenzy 時(shí)間: 2025-3-25 10:46
Overview of Applications by TypeThe numerous illustrations given in Parts I and II aimed at showing many alternative ways to exploit data by automatic learning. In particular, the tool-box approach came to us as the natural way to exploit the large amounts of data within our large-scale applications to power system dynamic security assessment.作者: 媒介 時(shí)間: 2025-3-25 13:20 作者: maladorit 時(shí)間: 2025-3-25 16:21
2196-3185 ive subset of automatic learningmethods - basic and more sophisticated ones - availablefrom statistics (both classical and modern), and from artificialintellige978-1-4613-7489-3978-1-4615-5451-6Series ISSN 2196-3185 Series E-ISSN 2196-3193 作者: Toxoid-Vaccines 時(shí)間: 2025-3-25 20:57
Introductionic information (knowledge) from data bases containing large amounts of low level data. The researchers in the field are statisticians and computer scientists, but also psychologists and neuro-physiologists. The latter’s aim is mainly to understand human learning abilities and they use automatic lear作者: 打火石 時(shí)間: 2025-3-26 04:11 作者: transient-pain 時(shí)間: 2025-3-26 06:28 作者: tendinitis 時(shí)間: 2025-3-26 12:18 作者: 松軟 時(shí)間: 2025-3-26 15:17
Framework for Applying Automatic Learning to Dynamic Security Assessmentific way. Although there are many different more or less sophisticated tools, the most widely accepted one is numerical simulation. In planning or operations planning departments, engineers thus use numerical simulation and their own expertise to run some scenarios and extract, by hand, the relevant作者: Minuet 時(shí)間: 2025-3-26 20:51 作者: 合同 時(shí)間: 2025-3-26 22:39 作者: 愛(ài)哭 時(shí)間: 2025-3-27 02:26
Future Orientations. We start by describing probabilistic security assessment and its connection with the automatic learning framework. Then we discuss the need for enhancing automatic learning methods in order to exploit more efficiently temporal data. We conclude by proposing a software architecture which should be 作者: 字形刻痕 時(shí)間: 2025-3-27 08:54
2196-3185 s, computer science, artificial intelligence, biologyand psychology. Its applications to engineering problems, such asthose encountered in electrical power systems, are thereforechallenging, while extremely promising. More and more data have becomeavailable, collected from the field by systematic ar作者: 鄙視 時(shí)間: 2025-3-27 11:04 作者: 橫條 時(shí)間: 2025-3-27 13:35 作者: Androgen 時(shí)間: 2025-3-27 20:57
Paulo Freire and Adult Education, to evaluate the approach on their systems. The second objective is to provide further illustrations of the type of results which may be obtained by a sample of the automatic learning methods described in the first part of the book.作者: Coeval 時(shí)間: 2025-3-28 00:15
Policies for Adult Learning in Scotlandproblems which are already solved or are in the process of being solved in the near future. Clearly, many engineers in Utilities will be skeptical and reluctant in using any new technology without strong motivations, especially as technical concerns tend to become secondary with respect to more commercial ones.作者: 粘 時(shí)間: 2025-3-28 06:07
https://doi.org/10.1057/9781137388254ncing automatic learning methods in order to exploit more efficiently temporal data. We conclude by proposing a software architecture which should be developed in order to enable the systematic use of the approach.作者: 老人病學(xué) 時(shí)間: 2025-3-28 09:17
Statistical Methodseristics of these methods is that they handle input information in the form of numerical attributes. Thus all non-numerical information must be translated into numbers via an appropriate coding scheme. In the case of power system problems, this concerns mainly the topological information, which is assumed to be coded by binary 0/1 indicators.作者: 巫婆 時(shí)間: 2025-3-28 11:51 作者: Generic-Drug 時(shí)間: 2025-3-28 17:47
A Sample of Real-Life Applications to evaluate the approach on their systems. The second objective is to provide further illustrations of the type of results which may be obtained by a sample of the automatic learning methods described in the first part of the book.作者: prodrome 時(shí)間: 2025-3-28 22:35 作者: bronchodilator 時(shí)間: 2025-3-29 01:21 作者: muffler 時(shí)間: 2025-3-29 05:28 作者: 委屈 時(shí)間: 2025-3-29 07:15
Nanotechnology Safety Educationiated only in the late eighties, when machine learning researchers started adopting probabilistic approaches and statisticians became interested in the new developments in the field of artificial neural networks.作者: 毛細(xì)血管 時(shí)間: 2025-3-29 13:58 作者: eulogize 時(shí)間: 2025-3-29 18:36
Machine Learningning capabilities of human beings; (ii) developing algorithms to reproduce them with the help of computers. One of the main motivations of the latter objective is the knowledge acquisition bottleneck encountered in the design of expert systems.作者: mettlesome 時(shí)間: 2025-3-29 21:12
Spencer L. James,Jane Rose Njuening capabilities of human beings; (ii) developing algorithms to reproduce them with the help of computers. One of the main motivations of the latter objective is the knowledge acquisition bottleneck encountered in the design of expert systems.作者: Jubilation 時(shí)間: 2025-3-30 00:29
Atomic Force Microscopy Educationficial intelligence. Related work in statistics dates back to Laplace [Lap 10] and Gauss [Gau26]. In the field of artificial neural networks, the early attempts were in the 1940’s [MP43], while work in symbolic machine learning emerged only in the mid sixties [HMS66].作者: INCH 時(shí)間: 2025-3-30 06:02
Introductionficial intelligence. Related work in statistics dates back to Laplace [Lap 10] and Gauss [Gau26]. In the field of artificial neural networks, the early attempts were in the 1940’s [MP43], while work in symbolic machine learning emerged only in the mid sixties [HMS66].作者: 地名詞典 時(shí)間: 2025-3-30 11:45 作者: Assignment 時(shí)間: 2025-3-30 15:37 作者: 宣誓書(shū) 時(shí)間: 2025-3-30 17:08
