Patent Innovation Factors Evolution Based on P-SVM and GCCA

Abstract:

A two-stage neural network architecture constructed by combining potential support vector machines (P-SVM) with genetic algorithm (GA) and gray correlation coefficient analysis (GCCA) is proposed for patent innovation factors evolution. The enterprises patent innovation is complex to conduct due to its nonlinearity of influenced factors. It is necessary to make a trade off among these factors when some of them conflict firstly. A novel way about nonlinear regression model with the potential support vector machines (P-SVM) is presented in this paper. In the model development, the genetic algorithm is employed to optimize P-SVM parameters selection. After the selected key factors by the PSVM with GA model, the main factors that affect patent innovation generation have been quantitatively studied using the method of gray correlation coefficient analysis. Using a set of real data in China, the results show that the methods developed in this paper can provide valuable information for patent innovation management and related municipal planning projects.

• Periodical: Applied Mechanics and Materials (Volume 39)
• Pages: 247-252
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.39.247
• Online since: November 2010
• Authors: Sheng Xu, Zhi Juan Wang, Hui Fang Zhao
• Keywords: Evolution, Genetic Algorithm (GA), Gray Correlation Coefficient Analysis, Potential Support Vector Machine

References

[1] P. Hingley, M. Nicolas: Methods for forecasting numbers of patent applications at the European Patent Office. World Patent Information Vol. 3 (2004), no. 26, pp.191-204.

DOI: 10.1016/j.wpi.2003.12.006

[2] V. N. Vapnik: An Overview of Statistical Learning Theory. IEEE Trans. Neural Networks Vol. 5 (1999), no. 10, pp.988-999.

DOI: 10.1109/72.788640

[3] Sheng Xu, Huifang Zhao and Xuanli Lv: A Grey SVM based model for patent application filings forecasting. Fuzzy Systems (2008), p.225 – 230.

DOI: 10.1109/fuzzy.2008.4630369

[4] I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh.: Nonlinear feature selection with the Foundations and Applications in Feature extraction. Eds. Springer (2005).

[5] Shengwei Fei, Yu Sun: Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm. Electric Power Systems Research Vol. 78 (March 2008), Issue 3, pp.507-514.

DOI: 10.1016/j.epsr.2007.04.006

[6] S. Hochreiter, K. Obermayer: Support vector machines for dyadic data. Neural Computation Vol. 18 (2006), no. 6, pp.1472-1510.

DOI: 10.1162/neco.2006.18.6.1472

[7] Xuesong Guo, Zhiping Yuan, Bojing Tian: Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications Vol. 36 (April 2009), Issue 3, Part 2, pp.6978-6985.

DOI: 10.1016/j.eswa.2008.08.074

[8] J. L. Deng: Control problems of gray systems. Syst. Control. Lett Vol. 2 (1982), no. 5, pp.288-294.

[9] Gray correlation analysis and prediction models of living refuse generation in Shanghai city. Waste Management Vol. 27 (2007), no. 3, pp.345-351.

DOI: 10.1016/j.wasman.2006.03.010