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- W88493265 abstract "Publisher Summary In the glass making industry, the quality of bottle rims is of great importance. Uneven or “wavy” rims on the bottles can lead to the deterioration of the contents held in the bottles because of air ingress. A means of using such data to classify bottles already exists through the use of a multi-layer perceptron (MLP) neural network with 100 inputs, a single 3 neuron hidden layer, and a single neuron output layer. A classification success rate of 95% has been recorded. However, because of the large number of inputs and complex algorithm used, this required neural networks that were unpractical for real time analysis. Applying such a neural network to classify bottles in real-time may cause problems because of the amount of calculation involved. This chapter describes a way of simplifying the classification neural network by first finding a Least Mean Square (LMS) filter to model the pressure profile." @default.
- W88493265 created "2016-06-24" @default.
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- W88493265 date "1996-01-01" @default.
- W88493265 modified "2023-09-27" @default.
- W88493265 title "Classification of bottle rims using Neural Networks - an LMS approach" @default.
- W88493265 doi "https://doi.org/10.1016/b978-044482587-2/50082-3" @default.
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