A Non-destructive Method Based on QPSO-RBF for the Measurement of Sugar Content in Cantaloupe
A nondestructive measurement approach is presented in this paper,which is capable of determining sugar content in cantaloupe from the dielectric property.The approach is based on measured equivalent capacitance and equivalent resistance of the cantaloupe,and on data analysis using quantum-behaved particle swarm optimization (QPSO) and Grey radial basis function (RBF) neural network.First,accumulated generating operation (AGO) in Grey forecasting is used to convert the initial observed data to obtain the accumulated data with strong regularity,which are employed to model and train the radial basis function neural network.Second,it adopted quantum-behaved particle swarm optimization algorithm to train the centers and widths of radial basis function.This model not only prevented the problem that the parameters of neural network are hard to be tuned,but also improved the network precision of prediction.Experimental results revealed that the predictive model as proposed has good predictive effect for the measurement of sugar in cantaloupes.
Quantum-behaved particle swarm optimization accumulated generating operation radial basis function neural network nondestructive testing
Lili Gao Zhenhong Jia Xizhong Qin Xiaohui Huang Yongbo Yao
College of Information Science and Engineering,Xinjiang University,Urumqi 830046,China
国际会议
沈阳
英文
505-509
2012-07-27（万方平台首次上网日期，不代表论文的发表时间）