Classification of mixed acoustic emission signals via neural networks

Jian Yang; Guy D. Dumont

doi:10.1117/12.140070

16 September 1992 Classification of mixed acoustic emission signals via neural networks

Jian Yang, Guy D. Dumont

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.140070
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

In order to offer operating information to the monitoring system of a chip refiner, a neural network classifier is proposed for identifying the acoustic emission signals of wood species. In addition to classifying single wood species, the system is required to be able to recognize mixed species. The classification task is accomplished by a multilayer feedforward neural network in which both supervised and unsupervised learning are included. The simulations are run on the testing data, mixing two single species to represent mutually mixed wood species of five categories. If a signal is identified as a mixture, the network will indicate the corresponding component species according to a lookup table. Some expected classification accuracy is obtained on both single and mixed species identification and performance of classification is discussed based on the simulation results.

Citation Download Citation

Jian Yang and Guy D. Dumont "Classification of mixed acoustic emission signals via neural networks", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.140070

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