量子遗传算法优化RBF神经网络及其在热工辨识中的应用

Thermal Process Identification With Radial Basis Function Network Based on Quantum Genetic Algorithm

量子遗传算法是基于量子计算原理的概率优化方法,在量子门更新过程中,旋转角的大小直接影响优化的结果和进化的速度。文中针对模糊量子遗传算法(FQGA)容易导致系统陷入局部最优的缺点,将量子衍生交叉算法的思想引入FQGA,提出了一种新的量子遗传算法。同时利用该方法构造径向基函数神经网络进行非线性系统辨识。其特点是通过这种新的量子遗传算法,实现对RBF神经网络权值、宽度和中心位置等有关参数的估计。其速度快、精度高。通过RBF神经网络有效地完成了对非线性系统的辨识。对典型非线性函数辨识的测试表明: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。同时利用该方法设计了一种通用的热工对象模型辨识神经网络算法,编制了专用的模型识别软件,对某电厂循环流化床锅炉一次风对床温的动态特性进行辨识,结果表明该方法是一种精度比较高的辨识算法。

Quantum genetic algorithm is a probability optimization method which is based on quantum compute principle. The precision and the rate of convergence are impacted by rotation angle. Aiming at the shortcoming of fuzzy quantum genetic algorithm （FQGA）, quantum-inspired crossover method was introduced to FQGA, and a novel quantum genetic algorithm was put forward. Using this method, an identification algorithm of nonlinear systems was presented. This method is characterized by estimating parameters such as weight, width and central position of RBFNN using the new quantum genetic algorithm. High velocity and accuracy of the method enable nonlinear systems to be efficiently identified by using RBFNN. The results of identifying typical nonlinear function demonstrate that the precision and the rate of convergence are improved. A special program was compiled to identify the object model of the thermal process, and the dynamic process between primary air feed rate and bed temperature was identified. The results show that accuracy of the approach is high.