Application of GA-improved Wavelet Network in Temperature Compensation of Sensor
Precise identification of the temperature compensation of sensor is of significance for the improvement of the precise testing of the system. Neural network has such capacities as self-learning, adaptive and non-linear mapping. However, it is often slow in training speed, and is easy to be trapped in local minimum value. While, genetic algorithm (GA) has very strong, global optimization searching capability but it is insufficient in local searching. This paper has explored the utilization of GA-improved wavelet neural network to obtain the global optimal solution. The measured data under multiple temperature conditions have been referred to so as to carry out effective identification of the temperature compensation model of eddy current sensor. The result shows that this method is quick in operation, high in precision and strong in generality. It has very promising application prospect in the areas such as intelligent sensor modeling and compensation.
Sensor Temperature Compensation Genetic Algorithm Wavelet Neural Network
ZHAO Hong MI Yanhua LIU Lixin
School of Mechatronic Engineering, China Jiliang University, Hangzhou 310018, P. R. China Zhejiang College of Construction, Hangzhou 311231, P. R. China Jixi University, Jixi, P. R. China
国际会议
The 29th Chinese Control Conference(第二十九届中国控制会议)
北京
英文
1-5
2010-07-29（万方平台首次上网日期，不代表论文的发表时间）