Title :
The Sync Tracing Based on Improved Genetic Algorithm Neural Network
Author :
Hou, Yuanbin ; Song, Chunfeng ; Li, Ning
Author_Institution :
Sch. of Electr. & Control, Xi´´an Univ. of Sci. & Technol., Xi´´an, China
Abstract :
The elevator system is important in mine safety manufacture. Aiming the character of frequent startup and stop with nonlinearity, the sync tracing method based on improved genetic algorithm neural network is presented. Because the condition of the normal adaptation function is too free, the adaptation function is improved, which is the new function altering with input space, then, improved genetic algorithm neural network (IGANN) is established, the IGANN not only avoids getting into local extremum point, but also realizes sync tracing. It is proved by simulation of 400 kW assistant elevator in nine, that the sync tracing IGANN is effective for the character of frequent startup and stop with nonlinearity.
Keywords :
genetic algorithms; lifts; mining; mining equipment; neural nets; elevator system; improved genetic algorithm neural network; mine safety manufacture; normal adaptation function; sync tracing method; Control systems; Elevators; Genetic algorithms; Genetic engineering; Information security; Intelligent networks; Manufacturing; Neural networks; Safety devices; Thermal stresses; improved genetic algorithm neural network; nine elevator; safety manufacture; sync tracing;
Conference_Titel :
Information Assurance and Security, 2009. IAS '09. Fifth International Conference on
Conference_Location :
Xian
Print_ISBN :
978-0-7695-3744-3
DOI :
10.1109/IAS.2009.226