ELM and Map Reduce has an unparalleled advantage of other similar technologies,which attract widely attention in machinelearning and distributed data processing communities *** this paper,we combine the advantage of ...
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ISBN:
(纸本)9781509036202
ELM and Map Reduce has an unparalleled advantage of other similar technologies,which attract widely attention in machinelearning and distributed data processing communities *** this paper,we combine the advantage of ELM and Map Reduce,and propose a distributed extreme learning machine based on Map Reduce framework(DELMM),which makes full use of the parallel computing ability of Map Reduce framework and realizes efficient learning of large-scale training *** particular,we present a spectral-spatial DELMM-based classifier for hyperspectral remote sensing images that integrates the information provided by extended morphological *** proposed spectral-spatial classifier allows different weights for both(spatial and spectral) features outperforming other ELMbased classifiers in terms of accuracy for land cover *** accuracy classification results are also better than those obtained by equivalent spectral-spatial SVM-based classifiers.
Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex m...
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Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed.
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