Title :
Key shape recognition algorithm based on genetic neural network
Author :
Yang, Hong-Tao ; Li, Hui ; Li, Xiu-Lan ; Zhao, Dan-Dan
Author_Institution :
Inst. of Electr. & Electron. Eng., Changchun Univ. of Technol., Changchun, China
Abstract :
To avoid the BP (Back-Propagation) Network´s disadvantages of low training speed, prone to trapping in a local optimum and poor capability of global search, this paper establishes the model of key based on generic algorithm with the research on the key shape, by optimizing the initialized weights and threshold of neural network with GA. After the test of the program complied by MATLAB language and the comparison with pure BP algorithm, the results show that the methods suggested by this paper improve both the accuracy of predicting and the rate of convergence.
Keywords :
backpropagation; genetic algorithms; neural nets; shape recognition; BP; MATLAB language; backpropagation; generic algorithm; genetic neural network; shape recognition algorithm; Presses; Training; BP Neutral Network; Generic Algorithm; Key shape recognition;
Conference_Titel :
Computer, Mechatronics, Control and Electronic Engineering (CMCE), 2010 International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4244-7957-3
DOI :
10.1109/CMCE.2010.5610145