WU Jian(吴健), ZHANG Zhi-jie(张志杰)
(Key Laboratory of Instrumentation Science & Dynamic Measurement, Ministry of Education, North University of China, Taiyuan 030051, China)
Abstract：The method based on particle swarm optimization（PSO） integrated with functional link articial neural network (FLANN) for correcting dynamic characteristics of sensor is used to reduce sensor’s dynamic error caused by its system limitations. Combining the advantages of PSO and FLANN, with this method a dynamic compensator can be realized without knowing the dynamic model of the sensor. According to the input and output of the sensor and the reference model, the weights of the network trained were used to initialize one particle station of the whole particle swarm when the training of the FLANN had been finished. Then PSO algorithm was applied, and the global best particle station of the particle swarm was the parameters of the compensator. The feasibility of dynamic compensation method is tested. Simulation results from simulator of sensor show that the results after being compensated have given a good description to input signals.
Key words：particle swarm optimization （PSO）; functional link articial neural network（FLANN）; dynamic error; dynamic compensation
CLD number： TP212.6 Document code： A
Article ID： 1674-8042(2012)01-0075-05 doi： 10.3969/j.issn.1674-8042.2012.01.016
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