【摘要】 水体富营养化是藻类爆发性生长的主要因素,为了对其进行实时监测预报,提出一种改进遗传神经网络(QGANN),以实现智能预测.该网络从遗传算法(GA)和神经网络(NN)两方面及其相互关系着手,构造了一个基于量子力学原理的量子平衡交叉算子,设计了一种NN混合优化策略,将两者合并共生获得了一类快速、高效的神经网络预测模型.水库和湖泊蓝绿藻爆发预测实验表明:该改进遗传算法(QGA)性能优良;QGANN的泛化能力明显提高,比未经改进的方法(GAsNN)及简单改进的方法(DCGANN)取得了更加满意的效果.更多还原

【Abstract】 In order to inspect and forecast rich nourishment and growth of blue-green algae,an improved genetic neural network(QGANN) was proposed,in which a quantum balance crossover operator for genetic algorithm(QGA) is developed and a hybrid optimization method based on neural network(NN) is introduced.The experimental results of forecasting algae in lakes and reservoirs indicate that the proposed QGA has a good performance,the QGANN can enhance modeling speed and boost the generalization performance of NN,and has more satisfactorily effect than basic genetic neural network(GAsNN) and improved genetic neural network(DCGANN).更多还原