An obsolescence forecasting method based on improved radial basis function neural network

Ain Shams Engineering Journal

卷:13

Yan Liu¹  Min Zhao² 

[1] Department of Computer Science, College of Engineering, Shantou University, Shantou 515063, China;

[2] School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China;

关键词: Obsolescence forecasting;    RBF neural network;    Radial basis function parameters;    Weight;    ELECTRE I;   

DOI  :  

来源: DOAJ

【 摘 要 】

With the rapid development of technologies, product lifecycle becomes shorter, which brings great challenges to obsolescence management. An efficient obsolescence forecasting method is in need. This research proposes a two-stage obsolescence forecasting model. The first stage identifies the key product features for obsolescence with ELECTRE I method. The second stage calculates the obsolescence probability based on radial basis function neural network. Three improvements are made for better predication accuracy, (1) information gain and information gain ratio are integrated to calculate the input weights; (2) an improved Particle Swarm Optimization algorithm is applied to calculate the clustering centroid; and (3) an improved gradient descent method determines the weights from hidden layer to output layer. The performance of the proposed method is compared with the existing model by using mobile dataset which contains 7000 samples. The experimental result shows that the accuracy of the predication has been improved from 92.2% to 95.23%.

【 授权许可】

Unknown