Multilayer feedforward networks are universal approximators
Neural Networks
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Immune algorithms-based approach for redundant reliability problems with multiple component choices
Computers in Industry - Special issue: Application of genetics algorithms in industry
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
Expert Systems with Applications: An International Journal
Process optimization of gold stud bump manufacturing using artificial neural networks
Expert Systems with Applications: An International Journal
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
An interactive co-evolutionary CAD system for garment pattern design
Computer-Aided Design
Expert Systems with Applications: An International Journal
ACS'08 Proceedings of the 8th conference on Applied computer scince
Extracting rules for classification problems: AIS based approach
Expert Systems with Applications: An International Journal
An immune co-evolutionary algorithm based approach for optimization control of gas turbine
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Knowledge and Information Systems
Expert Systems with Applications: An International Journal
Process integrated wire-bond quality control by means of cytokine-Formal Immune Networks
Journal of Intelligent Manufacturing
Fuzzy TOPSIS for multiresponse quality problems in wafer fabrication processes
Advances in Fuzzy Systems - Special issue on Advanced Fuzzy Methods in Decision Making and Data Analysis
Hi-index | 12.06 |
The wire bonding process is the key process in an IC chip-package. It is an urgent problem for IC chip-package industry to improve the wire bonding process capability. In this study, an integration of artificial neural networks (ANN) with artificial immune systems (AIS) is proposed to optimize parameters for an IC wire bonding process. The algorithm of AIS with memory cell and suppressor cell mechanisms is developed. The back-propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and responses. Then a Taguchi method is applied to identify the critical parameters of AIS. Finally, the AIS algorithm is applied to find the optimal parameters by using the output of ANN as the affinity measure. A comparison between the result of the proposed AIS and that of a genetic algorithm (GA) is conducted in this study. The comparison shows that the searching quality of the proposed AIS is more effective than the GA in finding the optimal wire bonding process parameters.