模糊量子遗传算法及其在热工过程模型辨识中的应用

Fuzzy Quantum Genetic Algorithm and Its Application Research in Thermal Process Identification

针对量子遗传算法(QGA)中旋转变异角相对固定的缺点,将模糊自适应的思想引入QGA,提出了模糊量子遗传算法(FQGA)。对典型函数测试表明:该方法有效地提高了量子遗传算法的计算精度和收敛速度。同时利用这种模糊量子遗传算法设计了一种通用的热工对象模型辨识算法,并编制了专用的模型识别软件,对典型热工过程进行辨识,取得了令人满意的效果。最后对某电厂循环流化床锅炉一次风对床温的传递函数进行辨识,结果表明该方法是一种简单易行的辨识算法,具有实用价值。

Aiming at the disadvantage of the immobile rotated angle in quantum genetic algorithm （QGA）, the fuzzy self tuning method was introduced and the fuzzy quantum genetic algorithm （FQGA） was put forward. The results of testing typical function demonstrate that the precision and the rate of convergence are improved by FQGA. A special program was compiled to identify the transfer function of thermal processes, and the effect is satisfied. Moreover, the transfer functions between primary air and bed temperature in a circulating fluidized bed boiler was identified. The results show the approach is easy to be used in identification and has a certain practical value.