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Titlebook: Wavelet Applications in Chemical Engineering; Rodolphe L. Motard,Babu Joseph Book 1994 Springer Science+Business Media New York 1994 algor

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樓主: Wilder
51#
發(fā)表于 2025-3-30 11:05:31 | 只看該作者
Trend Analysis Using the Frazier-Jawerth Transform,or its on-line implementation. Algorithms for FJ decomposition and reconstruction of 1-D signals are also included. FJT bears close resemblance to the wavelet transform technique which is enjoying much attention lately (Science, August 1990). The theory of frames has been shown to subsume the FJ and
52#
發(fā)表于 2025-3-30 16:03:57 | 只看該作者
53#
發(fā)表于 2025-3-30 19:57:46 | 只看該作者
Process Signal Features Analysis,concepts are defined to form a theoretical framework of this method. Several possible applications are discussed. This approach offers the potential for building new intelligent process signal analysis systems to identifying the process “finger prints”, i.e. the hidden time-frequency structure in si
54#
發(fā)表于 2025-3-31 00:09:07 | 只看該作者
Use of Wavelets for Numerical Solution of Differential Equations,imate a functions not by cancellation, but through placement of the right wavelets at appropriate locations. The multi-resolution analysis (MRA) properties of wavelets render them attractive candidates for functions in terms of which numerical solutions of differential equations can be represented.
55#
發(fā)表于 2025-3-31 03:06:34 | 只看該作者
Learning at Multiple Resolutions: Wavelets as Basis Functions in Artificial Neural Networks, and Inescribe the application of wavelets for multi-resolution learning in artificial neural networks and inductive decision trees, and show how wavelets may provide a unifying framework for various supervised learning techniques. A . is an artificial neural network with activation functions derived from
56#
發(fā)表于 2025-3-31 07:37:28 | 只看該作者
Application of Wavelets in Process Control,velets are discussed in the context of control applications. We also discuss various control problems where wavelets could be particularly advantageous. We illustrate the benefits of wavelet formulations by presenting wavelet domain approaches to basis reduction and frequency domain tuning in model
57#
發(fā)表于 2025-3-31 12:43:35 | 只看該作者
58#
發(fā)表于 2025-3-31 17:23:01 | 只看該作者
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