Recognition of Paper Currency Research Based on AGA-BP Neural Network

2014 ◽  
Vol 989-994 ◽  
pp. 3968-3972
Author(s):  
Xue Xiao ◽  
Qing Hong Wu ◽  
Ying Zhang

The genetic algorithm is a randomized search method for a class of reference biological evolution of the law evolved, with global implicit parallelism inherent and better optimization. This paper presents an adaptive genetic algorithm to optimize the use of BP neural network method, namely the structure of weights and thresholds to optimize BP neural network to achieve the recognition of banknotes oriented. Experimental results show that after using genetic algorithms to optimize BP neural network controller can accurately and quickly achieved recognition effect on banknote recognition accuracy compared to traditional BP neural network has been greatly improved, improved network adaptive capacity and generalization ability.

2012 ◽  
Vol 490-495 ◽  
pp. 1723-1727
Author(s):  
Jun Ting Wang ◽  
Guo Ping Liu ◽  
Wei Jin ◽  
Gen Fu Xiao

In the paper the mathematical model of the single inverted pendulum is established, on the base of the root locus and the control tasks the control system is made up of double closed-loop unit gain negative feedback and BP neural network controller. The results show that the inverted pendulum is efficiently controlled.


2013 ◽  
Vol 328 ◽  
pp. 72-76
Author(s):  
Huan Xin Cheng ◽  
Dao Sheng Zhang ◽  
Li Cheng

The traditional PID control, which is based on linearization, is often hard to obtain the optimal control effect on such nonlinear, multiple-output, strongly coupled systems like inverted pendulum. To solve the problem above, the BP neural network controller was developed for inverted pendulum. On the basis of establishing and analyzing the mathematical model of single inverted-pendulum, this paper established the state space expression, and then designed a neural network control system based on BP algorithm. The simulation was researched by Matlab and the running results show that this control has good robustness and can achieve satisfactory control effect.


2011 ◽  
Vol 403-408 ◽  
pp. 191-195
Author(s):  
Yong Chao Zhang ◽  
Wen Zhuang Zhao ◽  
Jin Lian Chen

How fuzzy technology and neural networks and genetic algorithm combine with each other has become the focus of research. A fuzzy neural network controller was proposed based on defuzzification and optimization around the fuzzy neural network structure. Genetic algorithm of fuzzy neural network was brought forward based on optimal control theory. Optimal structure and parameters of fuzzy neural network controller were Offline searched by way of controller performance indicators of genetic algorithm. Fuzzy neural network controller through genetic algorithm was accessed in fuzzy neural network intelligent control system.


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