Abstract
High-accuracy detection of environmental pollution (DEEP) schemes to measure a variety of pollutants arouses great interests in industry and research communities. This paper proposes a novel DEEP approach to improve the detection precision by using machine learning theory in which an RBF network for detection is optimized by genetic algorithm. Specifically, this cooperative scheme employs more appropriate relationship in the networks, which can accelerate the convergence of the algorithm and also can enhance the precision. Simulation results demonstrate that the proposed method outperforms conventional schemes in terms of environmental pollution detection accuracy, as well as monitoring different pollutants.
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Zhang, Y. (2020). DEEP: Detection of Environmental Pollution Using Cooperative Neural Network. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-13-6508-9_2
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DOI: https://doi.org/10.1007/978-981-13-6508-9_2
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