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Titlebook: Nonlinear Dynamics and Control in Process Engineering — Recent Advances; G. Continillo,M. Giona,S. Crescitelli Conference proceedings 2002

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11#
發(fā)表于 2025-3-23 11:09:42 | 只看該作者
C. Montana Lampo,V. Bartolozzi,A. Picciotto,M. Galluzzo alternatives were specified by parametric models. In the present chapter, we shall take up the crucial problem of testing the validity of such models, the hypothesis of .. For example, we would like to know whether a set of measurements ..,…,.. is consonant with the assumption that the .’s are an i
12#
發(fā)表于 2025-3-23 15:47:32 | 只看該作者
A. P. Reverberi,L. Toro,F. Vegliò at least partly unknown. Of the parameter ., which labels the distribution, it is assumed known only that it lies in a certain set Ω, the . is concerned with methods of using this observational material to obtain information concerning the distribution of . or the parameter . with which it is label
13#
發(fā)表于 2025-3-23 21:24:33 | 只看該作者
14#
發(fā)表于 2025-3-24 00:05:47 | 只看該作者
F. Sarghini,S. Santiniveals that the provision of examinations was as controversial in the nineteenth century as it is today, particularly since the government is now determined to bring in reform. The issues of grade inflation, the place of coursework in marking, and the introduction of technological change all feature
15#
發(fā)表于 2025-3-24 04:55:28 | 只看該作者
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發(fā)表于 2025-3-24 09:59:48 | 只看該作者
17#
發(fā)表于 2025-3-24 13:27:24 | 只看該作者
18#
發(fā)表于 2025-3-24 16:31:08 | 只看該作者
Qualitative Modeling and Identification,rity, time delay, dominant poles). In this contribution, three different procedures are proposed for modeling plants in terms of generic Wiener/Hammerstein nonlinear models with time delay. In order of increasing generality, the procedures include an extension of the Nonlinear AutoTune Variation met
19#
發(fā)表于 2025-3-24 19:36:31 | 只看該作者
Modelling Chaos with Neural Networks,en presented. The first, is a Locally Recurrent Neural Network that, keeping the feedforward architecture of the MLP, replaces the classical synapses with Finite Impulse Response and Infinite Impulse Response filters. The second, is a novel dynamic neural network obtained by making recurrent the neu
20#
發(fā)表于 2025-3-25 01:26:40 | 只看該作者
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