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Water Quality Sensor Model Based on an Optimization Method of RBF Neural Network

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Document pages: 11 pages

Abstract: In order to solve the problem that the traditional radial basis function (RBF)neural network is easy to fall into local optimal and slow training speed inthe data fusion of multi water quality sensors, an optimization method of RBFneural network based on improved cuckoo search (ICS) was proposed. The methoduses RBF neural network to construct a fusion model for multiple water qualitysensor data. RBF network can seek the best compromise between complexity andlearning ability, and relatively few parameters need to be set. By using ICSalgorithm to find the best network parameters of RBF network, the obtained network model can realize the non-linear mapping betweeninput and output of data sample. The data fusion processing experimentwas carried out based on the data released by Zhejiang province surface waterquality automatic monitoring data system from March to April 2018. Comparedwith the traditional BP neural network, the experimental results show that theRBF neural network based on gradient descent (GD) and genetic algorithm (GA),the new method proposed in this paper can effectively fuse the water qualitydata and obtain higher classification accuracy of water quality.

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