Title :
The forecast model of patents granted in colleges based on genetic neural network
Author :
Zang, Xijie ; Niu, Yanxia
Author_Institution :
Coll. of Sci., Zhongyuan Univ. of Technol., Zhengzhou, China
Abstract :
In order to avoid shortcomings of the standard BP network algorithm, the forecast model of patents granted in colleges is constructed based on genetic neural network. Involving the advantages of GA and BP, the algorithm can simultaneously complete genetic selection within a solution space to find the optimal points. Then the BP algorithm searchs the best optimal result from those points by the direction of negative gradient. Thus it not only can avoid the BP algorithm into a local minimum and slow convergence etc, but also can overcome long search time, slow shortcomings of the GA caused by searching optimal solution in a similar form of exhaustive. Simulation indicates that the algorithm is more accuracy than the standard BP algorithm, faster incalculation and very well in applicability.
Keywords :
educational institutions; genetic algorithms; gradient methods; neural nets; patents; GA; colleges; genetic neural network; negative gradient; patents forecast model; standard BP network algorithm; Educational institutions; Genetic algorithms; Genetics; Patents; Prediction algorithms; Predictive models; Search problems; Patent; college; genetic algorithm; neural network; time series;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2011 International Conference on
Conference_Location :
Yichang
Print_ISBN :
978-1-4244-8162-0
DOI :
10.1109/ICECENG.2011.6057452