標(biāo)題: Titlebook: Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes; Krzysztof Patan Book 2008 Springer-Verlag Berlin [打印本頁(yè)] 作者: Cleveland 時(shí)間: 2025-3-21 16:34
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書目名稱Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes讀者反饋
書目名稱Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes讀者反饋學(xué)科排名
作者: mutineer 時(shí)間: 2025-3-21 23:40
Modelling Issue in Fault Diagnosis,chnical Committee SAFEPROCESS, have been introduced in order to unify the terminology in the area.. is an unpermitted deviation of at least one characteristic property or variable of the system from acceptable/usual/standard behaviour.. is a permanent interruption of the system ability to perform a 作者: leniency 時(shí)間: 2025-3-22 01:33 作者: 是貪求 時(shí)間: 2025-3-22 06:18
Approximation Abilities of Locally Recurrent Networks,ties, has been successfully applied to solve problems from different scientific and engineering areas. Cannas and co-workers [154] applied a locally recurrent network to train the attractors of Chua’s circuit, as a paradigm for studying chaos. The modelling of continuous polymerisation and neutralis作者: Indent 時(shí)間: 2025-3-22 09:46
Stability and Stabilization of Locally Recurrent Networks,ion to training algorithms adjusting the parameters of neural networks. If the predictor is unstable for certain choices of neural model parameters, serious numerical problems can occur during training. Stability criteria should be universal, applicable to as broad a class of systems as possible and作者: Scintillations 時(shí)間: 2025-3-22 16:25
Optimum Experimental Design for Locally Recurrent Networks,system behaviour [77, 7]. This problem comprises the determination of a limited number of observational units obtained from the experimental environment in such a way as to obtain the best quality of the system responses..The importance of input data selection has already been recognized in many app作者: CONE 時(shí)間: 2025-3-22 20:19 作者: 共同給與 時(shí)間: 2025-3-22 22:33 作者: pantomime 時(shí)間: 2025-3-23 03:31 作者: 服從 時(shí)間: 2025-3-23 08:54 作者: Ferritin 時(shí)間: 2025-3-23 13:47 作者: 運(yùn)動(dòng)吧 時(shí)間: 2025-3-23 15:43 作者: Lignans 時(shí)間: 2025-3-23 20:00 作者: patella 時(shí)間: 2025-3-24 00:16
https://doi.org/10.1007/BFb0108459ccording to which any continuous non-linear relation can be approximated with arbitrary accuracy using a neural network with a suitable architecture and weight parameters. Their another attractive property is the self-learning ability. A neural network can extract the system features from historical作者: 都相信我的話 時(shí)間: 2025-3-24 04:35 作者: engagement 時(shí)間: 2025-3-24 08:15
S. Hunklinger,H. Sussner,K. Dransfeldion to training algorithms adjusting the parameters of neural networks. If the predictor is unstable for certain choices of neural model parameters, serious numerical problems can occur during training. Stability criteria should be universal, applicable to as broad a class of systems as possible and作者: 公司 時(shí)間: 2025-3-24 13:42 作者: lipids 時(shí)間: 2025-3-24 14:54
Rudolf Sizmann,Constantin Varelas of the residual signal takes place and, subsequently, the decision about faults is made in the form of an alarm. The residual evaluation is nothing else but a logical decision making process that transforms quantitative knowledge into qualitative . statements [4, 9, 7]. It can also be seen as a cla作者: Mendicant 時(shí)間: 2025-3-24 21:01
Diffraction in surfaces and interfaces,ee examples are considered:.In all case studies, locally recurrent globally feedforward networks, introduced in Section 3.5.4, are used as models of the industrial processes considered. Other types of neural networks, discussed in Sections 7.2 and 7.3.3, are used in decision making in order to detec作者: 可卡 時(shí)間: 2025-3-24 23:49
Surface-dynamics of growing crystals,ing and identification of non-linear dynamic processes and fault diagnosis of technical processes. The self-learning ability and the property of approximating non-linear functions provide the modelling of non-linear systems with a great flexibility. These features allow one to design adaptive contro作者: Nostalgia 時(shí)間: 2025-3-25 06:24
Industrial Applications,ee examples are considered:.In all case studies, locally recurrent globally feedforward networks, introduced in Section 3.5.4, are used as models of the industrial processes considered. Other types of neural networks, discussed in Sections 7.2 and 7.3.3, are used in decision making in order to detect faults.作者: Presbycusis 時(shí)間: 2025-3-25 10:39
Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes978-3-540-79872-9Series ISSN 0170-8643 Series E-ISSN 1610-7411 作者: intention 時(shí)間: 2025-3-25 14:19
Diffraction in surfaces and interfaces,ee examples are considered:.In all case studies, locally recurrent globally feedforward networks, introduced in Section 3.5.4, are used as models of the industrial processes considered. Other types of neural networks, discussed in Sections 7.2 and 7.3.3, are used in decision making in order to detect faults.作者: LAVA 時(shí)間: 2025-3-25 19:10 作者: 大看臺(tái) 時(shí)間: 2025-3-25 21:21
Lecture Notes in Control and Information Scienceshttp://image.papertrans.cn/b/image/162675.jpg作者: 音樂(lè)會(huì) 時(shí)間: 2025-3-26 00:13 作者: Arboreal 時(shí)間: 2025-3-26 04:55
Ingolf Ruge,Helmut Müller,Heiner Ryssel faults that might occur in the supervised process renders it possible to perform important preventing actions. Moreover, it allows one to avoid heavy economic losses involved in stopped production, the replacement of elements and parts, etc.作者: 易受刺激 時(shí)間: 2025-3-26 10:56 作者: phase-2-enzyme 時(shí)間: 2025-3-26 15:49
Book 2008odes of - haviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and e?ective fault diagnosis strategies. This is especially true for engineering systems,whose complexity is permanently growing due to the inevitable development of modern作者: cleaver 時(shí)間: 2025-3-26 18:54 作者: padding 時(shí)間: 2025-3-26 21:20 作者: COLON 時(shí)間: 2025-3-27 01:10
Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes作者: Insubordinate 時(shí)間: 2025-3-27 06:12 作者: choleretic 時(shí)間: 2025-3-27 12:17 作者: 令人悲傷 時(shí)間: 2025-3-27 15:50
Approximation Abilities of Locally Recurrent Networks,tem [39], or model based fault diagnosis of sensor and actuator faults in a sugar evaporator [26]. Tsoi and Back [38] compared and applied different architectures of locally recurrent networks to the prediction of speech utterance. Finally, Campolucci and Piazza [156] elaborated an intristic stabili作者: goodwill 時(shí)間: 2025-3-27 19:43 作者: 不朽中國(guó) 時(shí)間: 2025-3-27 23:48
Optimum Experimental Design for Locally Recurrent Networks,al conditions in order to gather informative measurements can be very expensive or even impossible (e.g. for faulty system states). On the other hand, data from a real-world system may be very noisy and using all the data available may lead to significant systematic modelling errors. As a result, we作者: 人充滿活力 時(shí)間: 2025-3-28 05:49 作者: 變化 時(shí)間: 2025-3-28 08:38 作者: 貞潔 時(shí)間: 2025-3-28 13:52 作者: agenda 時(shí)間: 2025-3-28 17:07 作者: neutral-posture 時(shí)間: 2025-3-28 19:54 作者: 閹割 時(shí)間: 2025-3-29 00:16
S. Hunklinger,H. Sussner,K. Dransfeldasymptotic as well exponential stability conditions for discrete-time recurrent networks with globally Lipschitz continuous and monotone nondecreasing activation functions were introduced by Hu and Wang [170]. The existence and uniqueness of an equilibrium were given as a matrix determinant problem.
