Prediction of the Properties of an Alumina Green Body Using an Artificial Neural Network by a New PSO-Gain Backpropagation Algorithm
Artificial neural networks have been successfully used in classification,formulation optimization,defect diagnosis and performance prediction in ceramic industry.However,an artificial neural network based on the traditional backpropagation (BP) algorithm showed some disadvantages in mapping the nonlinear relationship between the composition and contents of the ceramic materials and their properties.In this paper,a new PSO-Grain (Particle Swarm Optimization Gain) BP algorithm was introduced,and an improved artificial neural network model was employed to predict the properties of an alumina green body.The training performance of the neural network using the PSO-Gain BP algorithm was analyzed and it was indicated the POS-Gain BP based neural network could reduce convergence to local minima and was more efficient than the traditional BP based network.The prediction accuracy of the properties such as linear shrinkage and bending strength using the PSO-Gain BP based neural network was higher than that of the BP based neurat network.
Alumina green body Neural network Particle swarm optimization Backpropagation
Li Bing Zhong Huiling Li Hongjie Chen Ling Li Lin Li Xiaoxi
Institute of Light Industry and Chemical Engineering,South China University of Technology,Guangzhou College of Electronical Business,South China University of Technology,Guanghou 510640,China
国际会议
The Fifth China International Conference on High-Performance Ceramics (第五届先进陶瓷国际研讨会(CICC-5))
长沙
英文
1642-1644
2007-05-10（万方平台首次上网日期，不代表论文的发表时间）