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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1406

Title: Modelling the development of fluid dispensing for electronic packaging : hybrid particle swarm optimization based-wavelet neural network approach
Authors: Ling, S. H.
Iu, Herbert Ho-ching
Leung, Frank H. F.
Chan, K. Y.
Subjects: Electronics packaging
Image classification
Optimization
Particle swarm optimization (PSO)
Power transformers
Problem solving
Translation (languages)
Vegetation
Issue Date: 2008
Publisher: IEEE
Citation: IJCNN 2008 : proceedings of the International Joint Conference on Neural Networks : Hong Kong, China, June 1-6, 2008, p. 98-103.
Abstract: An hybrid Particle Swarm Optimization PSO-based wavelet neural network for modelling the development of fluid dispensing for electronic packaging is presented in this paper. In modelling the fluid dispensing process, it is important to understand the process behaviour as well as determine optimum operating conditions of the process for a high-yield, low cost and robust operation. Modelling the fluid dispensing process is a complex non-linear problem. This kind of problem is suitable to be solved by neural network. Among different kinds of neural networks, the wavelet neural network is a good choice to solve the problem. In the proposed wavelet neural network, the translation parameters are variables depending on the network inputs. Thanks to the variable translation parameters, the network becomes an adaptive one. Thus, the proposed network provides better performance and increased learning ability than conventional wavelet neural networks. An improved hybrid PSO [1] is applied to train the parameters of the proposed wavelet neural network. A case study of modelling the fluid dispensing process on electronic packaging is employed to demonstrate the effectiveness of the proposed method.
Rights: © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
URI: http://hdl.handle.net/10397/1406
ISBN: 978-1-4244-1821-3
Appears in Collections:EIE Conference Papers & Presentations
ISE Conference Papers & Presentations

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