標題: Titlebook: Data Mining and Knowledge Discovery for Process Monitoring and Control; Xue Z. Wang Book 1999 Springer-Verlag London 1999 Artificial Intel [打印本頁] 作者: 他剪短 時間: 2025-3-21 18:16
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作者: resilience 時間: 2025-3-21 23:44 作者: Contracture 時間: 2025-3-22 03:11
Mansi Patel,Jeel Padiya,Mangal Singhbe considered in applying FFNN. While the focus will be on FFNN, other supervised models will also be described and compared with FFNN. These include fuzzy FFNN, fuzzy set covering approach and fuzzy signed digraph.作者: Introduction 時間: 2025-3-22 07:37
1430-9491 y, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.978-1-4471-1137-5978-1-4471-0421-6Series ISSN 1430-9491 Series E-ISSN 2193-1577 作者: Emg827 時間: 2025-3-22 09:16 作者: 牢騷 時間: 2025-3-22 14:15 作者: 牢騷 時間: 2025-3-22 19:55
Supervised Learning for Operational Support,be considered in applying FFNN. While the focus will be on FFNN, other supervised models will also be described and compared with FFNN. These include fuzzy FFNN, fuzzy set covering approach and fuzzy signed digraph.作者: 記成螞蟻 時間: 2025-3-22 23:50
Data Mining and Knowledge Discovery for Process Monitoring and Control作者: Granular 時間: 2025-3-23 02:03
Xue Z. WangFeatures the subjects of on-line signal preprocessing, feature extraction and concept formation, operational state identification and automatic generation of decision trees and production rules from d作者: 整體 時間: 2025-3-23 07:07 作者: 精美食品 時間: 2025-3-23 11:19 作者: 伴隨而來 時間: 2025-3-23 14:34
https://doi.org/10.1007/978-1-4615-1327-8rmation and database technologies. With the increasing use of databases the need to be able to digest large volumes of data being generated is now critical. It is estimated that only 5%-10% of commercial databases have ever been analysed [23]. As Massey and Newing [24] indicated that database techno作者: 戲法 時間: 2025-3-23 20:16 作者: 租約 時間: 2025-3-24 01:15 作者: Provenance 時間: 2025-3-24 05:25
Mansi Patel,Jeel Padiya,Mangal Singhis clearly different from the kind of learning as learning to ride a bicycle. In supervised learning, each example used is typically described by a number of attributes. The attributes are divided into inputs and outputs, and the learning process is to develop a model mapping the multiple inputs and作者: 易達到 時間: 2025-3-24 07:31 作者: COLON 時間: 2025-3-24 12:47 作者: 尖牙 時間: 2025-3-24 16:08
Borka Jerman-Bla?i?,Toma? Klobu?arontrol of process variables. Some of the variables including flowrate, temperatures, pressures and levels can be easily monitored on-line with cost effective and reliable measuring devices. Some variables, however, are often analysed off-line in laboratories because they are either too expensive or 作者: 救護車 時間: 2025-3-24 22:02 作者: 使成核 時間: 2025-3-25 00:25 作者: Locale 時間: 2025-3-25 06:08
978-1-4471-1137-5Springer-Verlag London 1999作者: 正常 時間: 2025-3-25 10:39
Data Mining and Knowledge Discovery for Process Monitoring and Control978-1-4471-0421-6Series ISSN 1430-9491 Series E-ISSN 2193-1577 作者: 露天歷史劇 時間: 2025-3-25 15:20
Mustafa A. Al-Asadi,Sakir TasdemirThis chapter describes some representative unsupervised machine learning approaches for process operational state identification. Whether a machine learning approach is regarded as supervised or unsupervised depends on the way it makes use of prior knowledge of the data. Data encountered can be broadly divided into the following four categories:作者: 即席演說 時間: 2025-3-25 17:47 作者: 微塵 時間: 2025-3-25 23:57
Ivana Cvitanovi?,Marina Bagi? Babac systems. As distinguished from similarity or distance based clustering, such conceptual clustering is able to generate conceptual knowledge about the major variables which are responsible for clustering, as well as predicting operational states. The resulting knowledge is expressed in the form of production rules or decision trees.作者: albuminuria 時間: 2025-3-26 02:31 作者: 拋射物 時間: 2025-3-26 07:42
Inductive Learning for Conceptual Clustering and Real-Time Process Monitoring, systems. As distinguished from similarity or distance based clustering, such conceptual clustering is able to generate conceptual knowledge about the major variables which are responsible for clustering, as well as predicting operational states. The resulting knowledge is expressed in the form of production rules or decision trees.作者: 傷心 時間: 2025-3-26 09:35 作者: Ergots 時間: 2025-3-26 14:22 作者: FILTH 時間: 2025-3-26 18:43
1430-9491 tic generation of decision trees and production rules from dModern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with作者: aristocracy 時間: 2025-3-26 22:26 作者: PLIC 時間: 2025-3-27 02:25
Book 1999ods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.作者: 察覺 時間: 2025-3-27 08:27 作者: 輕快來事 時間: 2025-3-27 11:27
Borka Jerman-Bla?i?,Toma? Klobu?ar hours. Such a delay makes it too late to make timely adjustments. An inferential or software sensor can predict the primary output variable from process inputs and other measured variables without incurring the measurement delay. Such prediction is necessary for implementation of inferential control.作者: 混亂生活 時間: 2025-3-27 15:38 作者: maintenance 時間: 2025-3-27 19:14 作者: 雜役 時間: 2025-3-27 22:09
Inferential Models and Software Sensors, hours. Such a delay makes it too late to make timely adjustments. An inferential or software sensor can predict the primary output variable from process inputs and other measured variables without incurring the measurement delay. Such prediction is necessary for implementation of inferential control.作者: clarify 時間: 2025-3-28 02:26
Concluding Remarks,oint that plant operators and supervisors are part of the overall control system responsible for data interpretation and critical decision making, and therefore should be integrated into control systems in a way to provide them with necessary computer based, automatic processing tools.作者: 補角 時間: 2025-3-28 08:37
Book 1999erpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts an作者: prosperity 時間: 2025-3-28 13:01 作者: Seminar 時間: 2025-3-28 18:23 作者: Silent-Ischemia 時間: 2025-3-28 19:22
Data Pre-Processing for Feature Extraction, Dimension Reduction and Concept Formation,surements. The discussion is concerned with capturing the features in dynamic trend signals. A dynamic trend representation is the visualisation of the changing trajectory of a variable over time and consists of many sample values. However, in order to make effective use of trends in a computer base作者: Common-Migraine 時間: 2025-3-29 01:50
Multivariate Statistical Analysis for Data Analysis and Statistical Control,variate statistical control systems. The methods introduced in this chapter include principal component analysis (PCA), partial least squares (PLS), and multi-way and nonlinear PCA. The emphasis will be put on how these approaches can be applied to solving practical problems and addressing the advan作者: 極深 時間: 2025-3-29 06:37
