A Non-Destructive Method Based on QPSO-RBF for the Measurement of Sugar Content in Cantaloupe

Abstract:

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.

• Periodical: Advanced Materials Research (Volume 571)
• Pages: 505-509
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.571.505
• Online since: September 2012
• Authors: Li Li Gao, Zhen Hong Jia, Xi Zhong Qin, Xiao Hui Huang, Yong Bo Yao
• Keywords: Accumulated Generating Operation, Nondestructive Testing (NDT), Quantum-Behaved Particle Swarm Optimization, Radial Basis Function Neural Network (RBFNN)

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