A Comparison of Evolvable Hardware Architectures for Classification Tasks
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Evolvable Hardware: From Applications to Implications for the Theory of Computation
UC '09 Proceedings of the 8th International Conference on Unconventional Computation
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
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Evolvable hardware has shown to be a promising approach for prosthetic hand controllers as it features self-adaptation, fast training, and a compact system-on-chip implementation. Besides these intriguing features, the classification performance is paramount to success for any classifier. However, evolvable hardware classifiers have not yet been sufficiently compared to state-of-the-art conventional classifiers. In this paper, we compare two evolvable hardware approaches for signal classification to three conventional classification techniques: k-nearest-neighbor, decision trees, and support vector machines. We provide all classifiers with features extracted from electromyographic signals taken from forearm muscle contractions, and try to recognize eight different hand movements. Experimental results demonstrate that evolvable hardware approaches are indeed able to compete with state-of-the-art classifiers. Specifically, one of our evolvable hardware approaches delivers a generalization performance similar to that of support vector machines.