Grey SVM with Simulated Annealing Algorithms in Patent Application Filings Forecasting

Abstract

The patent applications filings (PAF) are complex to conduct due to its nonlinearity of influenced factors. In this study, a grey support vector machines with simulated annealing algorithms (GSVMS) is proposed to forecast PAF. In addition, GM (1, N) model of grey system is used to add a grey layer before neural input layer and white layer after SVM layer. Simulated annealing algorithms (SA) are used to determine free parameters of support vector machines. Evaluation method has been used for comparing the performance of forecasting techniques. The experiments show that the GSVMS model is outperformed GM (1, N) model and SVM with simulated annealing algorithms (SVMS) model, and PAF forecasting based on GSVMS is of validity and feasibility.