Abstract
An evolvable hardware (EHW) system for high-speed sonar return classification has been proposed. The system demonstrates an average accuracy of 91.4% on a sonar spectrum data set. This is better than a feed-forward neural network and previously proposed EHW architectures. Furthermore, this system is designed for online evolution. Incremental evolution, data buses and high level modules have been utilized in order to make the evolution of the 480 bit-input classifier feasible. The classification has been implemented for a Xilinx XC2VP30 FPGA with a resource utilization of 81% and a classification time of 0.5μs.
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Glette, K., Torresen, J., Yasunaga, M. (2007). An Online EHW Pattern Recognition System Applied to Sonar Spectrum Classification. In: Kang, L., Liu, Y., Zeng, S. (eds) Evolvable Systems: From Biology to Hardware. ICES 2007. Lecture Notes in Computer Science, vol 4684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74626-3_1
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DOI: https://doi.org/10.1007/978-3-540-74626-3_1
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