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Title: Improved hybrid particle swarm optimized wavelet neural network for modeling the development of fluid dispensing for electronic packaging
Authors: Ling, SH
Iu, HHC
Leung, FHF 
Chan, KY
Issue Date: Sep-2008
Source: IEEE transactions on industrial electronics, Sept. 2008, v. 55, no. 9, p. 3447-3460
Abstract: An improved hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for Modeling the development of Fluid Dispensing for Electronic Packaging (MFD-EP) is presented in this paper. In modeling the fluid dispensing process, it is important to understand the process behavior as well as determine the optimum operating conditions of the process for a high-yield, low-cost, and robust operation. Modeling the fluid dispensing process is a complex nonlinear problem. This kind of problem is suitable to be solved by applying a neural network. Among the different kinds of neural networks, the WNN is a good choice to solve the problem. In the proposed WNN, the translation parameters are variables depending on the network inputs. Due to the variable translation parameters, the network becomes an adaptive one that provides better performance and increased learning ability than conventional WNNs. An improved hybrid PSO is applied to train the parameters of the proposed WNN. The proposed hybrid PSO incorporates a wavelet-theory-based mutation operation. It applies the wavelet theory to enhance the PSO in more effectively exploring the solution space to reach a better solution. A case study of MFD-EP is employed to demonstrate the effectiveness of the proposed method.
Keywords: Modeling
Particle swarm optimization (PSO)
Wavelet neural network (WNN)
Wavelet theory
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on industrial electronics 
ISSN: 0278-0046
EISSN: 1557-9948
DOI: 10.1109/TIE.2008.922599
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.
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