Abstract
In order to monitor the abnormalities of porcine diet by porcine diet data, the judgment method of a porcine abnormal diet based on improved support vector data description (SVDD) and particle swarm optimization (PSO) is proposed. Because the penalty factor and kernel parameter of SVDD are difficult to determine and the accuracy for fuzzy decision by decision function of SVDD is limited, PSO is used to optimize the parameters of SVDD and fuzzy decision function is proposed to make the judgment for abnormalities of porcine diet. To avoid the local minimum problems, particle mutation is adopted to improve PSO. Firstly, the collected porcine diet data are normalized to build training data and test data. Secondly, a fuzzy decision function is built and improved PSO is adopted to optimize the parameters of SVDD. And then the judgment model of porcine abnormal diet is established. Finally, the porcine diet data are judged by the improved PSO-SVDD. The experimental results show that the proposed method obtains high accuracy for porcine diet data. It provides an effective method for the judgment of porcine abnormal diet through the diet data.
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This study was supported by the National High Technology Research and Development Program of China (863 Program) (2013AA102306).
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Zhang, S., Tian, J., Li, J. (2021). The Research on the Judgment Method for Porcine Abnormal Diet Based on Improved PSO-SVDD. In: Nakamatsu, K., Kountchev, R., Aharari, A., El-Bendary, N., Hu, B. (eds) New Developments of IT, IoT and ICT Applied to Agriculture. Smart Innovation, Systems and Technologies, vol 183. Springer, Singapore. https://doi.org/10.1007/978-981-15-5073-7_14
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DOI: https://doi.org/10.1007/978-981-15-5073-7_14
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