Titlebook: Artificial Neural Nets and Genetic Algorithms; Proceedings of the I David W. Pearson,Nigel C. Steele,Rudolf F. Albrech Conference proceedin

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Dustin Harp,Jaime Loke,Ingrid Bachmanndetection is then possible on-line or classes can be labelled to give diagnosis. This presentation explains the special nature of machine monitoring applications in data availability and desired diagnosis information and provides examples of such systems working in different environments.

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Lamea Elle Shaaban-Maga?a,Melanie L. Miller we propose using genetic algorithms to solve the relaxation labeling learning problem to overcome the difficulties with the gradient algorithm. Experiments are presented which demonstrate the superiority of the proposed approach both in terms of quality of solutions and robustness.

FADE

Kohonen Neural Networks for Machine and Process Condition Monitoringne major advantage over their more common peers in that they are capable of unsupervised learning. This property makes them ideal for machine health monitoring situations..Unsupervised learning allows the network to represent single or multiple classes according to distribution and density. Novelty

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Process Modelling and Control with Neural Networks: Present Status and Future Directions, mainly because of i) insufficient analytical knowledge of the system to be controlled, and ii) because of incomplete knowledge of the physical parameters of the system..Neural networks can cope with these problems because of their capability to realize multivariate, nonlinear transformations and t

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A Genetic Algorithm for Multicriteria Inventory Classificationrepresent the weights of criteria, where the sum of all weights is 1. A chromosome represents the values of the weights, possibly along with some cut-off points. A new crossover operation, called ., is proposed, such that it produces valid chromosomes given that the parent chromosomes are valid. The