Multiple-class identification algorithm using genetic neural networks

Rustom Mamlook; Wiley E. Thompson

doi:10.1117/12.213064

5 July 1995 Multiple-class identification algorithm using genetic neural networks

Rustom Mamlook, Wiley E. Thompson

Proceedings Volume 2484, Signal Processing, Sensor Fusion, and Target Recognition IV; (1995) https://doi.org/10.1117/12.213064
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

Multiple-class identification algorithm using genetic neural networks is presented. The algorithm uses a feedforward neural network so it is fast. The algorithm uses the Kohonen network to provide an unsupervised learning. The Kohonen network is used with Z-axis normalization. The weight initialization is done by genetic optimization to escape from local minima. The performance of the algorithm is evaluated using a confusion matrix method. The algorithm does not require the number of classes to be known a priori. It also provides a threshold selection method. An example is given to illustrate the application of the algorithm and to evaluate its performance.

Citation Download Citation

Rustom Mamlook and Wiley E. Thompson "Multiple-class identification algorithm using genetic neural networks", Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); https://doi.org/10.1117/12.213064

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