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The FPGA-based multi-classifier

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Abstract

To achieve intelligent real-time processing, the paper presents a weightless neural-based cognitive system which is capable of classifying, analysing, and prediction from sight or sound. The proposed cognitive system fused recursive-least-square (RLS) filters in parallel with an enhanced probabilistic convergent network (EPCN) serially—implemented on field programmable gate array. The novelty is that EPCN does not require an optimum result from RLS to achieve good responses from the RLS–EPCN fusion, thereby further offering two main distinguishing features: compactness and speed. Test results demonstrate RLS–EPCN’s suitability to exploration/exploitation of hostile surroundings such as sea exploration.

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Acknowledgments

Thanks to: SACLANT ASW Research Centre, La Spezia, Italy, for SONAR databases.

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  1. School of Engineering and Digital Arts, University of Kent, Canterbury, Kent, CT2 7NT, UK

    Pierre Lorrentz

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  1. Pierre Lorrentz
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Correspondence to Pierre Lorrentz.

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Lorrentz, P. The FPGA-based multi-classifier. Pattern Anal Applic 18, 207–223 (2015). https://doi.org/10.1007/s10044-014-0380-z

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