Abstract
A multi-structure neural network (MSNN) classifier consisting of four discriminators followed by a maximum selector was designed and applied to classification of four grades of pistachio nuts. Each discriminator was a multi-layer feed-forward neural network with two hidden layers and a single-neuron output layer. Fourier descriptors of the nuts' boundaries and their area were used as the recognition features. The individual discriminators were trained using a biased technique and a back-propagation algorithm. The MSNN classifier gave an average classification performance of 95.0%. This was an increase of 14.8% over the performance of a multi-layer neural network (MLNN) with similar complexity for classifying the same set of patterns.
Original language | English (US) |
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Pages (from-to) | 55-61 |
Number of pages | 7 |
Journal | International journal of neural systems |
Volume | 8 |
Issue number | 1 |
DOIs | |
State | Published - Feb 1997 |
Externally published | Yes |
ASJC Scopus subject areas
- Computer Networks and Communications