TITLE:
Water Quality Sensor Model Based on an Optimization Method of RBF Neural Network
AUTHORS:
Wei Huang, Yiwei Yang
KEYWORDS:
Water Quality Sensor, ICS Algorithm, RBF, Data Fusion, Nonlinear Mapping
JOURNAL NAME:
Computational Water, Energy, and Environmental Engineering,
Vol.9 No.1,
November
19,
2019
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 in
the data fusion of multi water quality sensors, an optimization method of RBF
neural network based on improved cuckoo search (ICS) was proposed. The method
uses RBF neural network to construct a fusion model for multiple water quality
sensor data. RBF network can seek the best compromise between complexity and
learning ability, and relatively few parameters need to be set. By using ICS
algorithm to find the best network parameters of RBF network, the obtained network model can realize the non-linear mapping between
input and output of data sample. The data fusion processing experiment
was carried out based on the data released by Zhejiang province surface water
quality automatic monitoring data system from March to April 2018. Compared
with the traditional BP neural network, the experimental results show that the
RBF neural network based on gradient descent (GD) and genetic algorithm (GA),
the new method proposed in this paper can effectively fuse the water quality
data and obtain higher classification accuracy of water quality.