【摘要】 水质评价对于对于水资源保护和管理拥有重要的指导意义。本次实验通过对BP神经网络、量子遗传算法以及粒子群算法的深入研究，实现了三者取长补短的有机结合，借助MATLAB软件搭建了基于量子遗传算法与粒子群算法混合优化的BP神经网络水质评价模型，解决了BP神经网络容易陷入局部最优解以及确定粒子群算法种群最佳初始状态的问题，同时结合了2008—2012年东江湖水质数据进行了仿真实验，验证了该模型的准确性和可行性。粒子群算法可以有效的让BP神经网络跳出局部最佳从全局分析水质等级，而且量子遗传算法的加入解决了粒子群算法初值选择的问题，让粒子群算法的优化能力达到最佳效果，提高了网络的评价精度和准确度，更加能反映出不同时间段水体的真实状况。本次实验一定程度上提升了传统水质评价的准确性和鲁棒性，为后续的水质相关研究提供了稳定可靠的工具。更多还原

【Abstract】 Water quality assessment plays a significant guiding role in the protection and management of water resources.This experiment, through in-depth research on BP neural network, quantum genetic algorithm and particle swarm optimization algorithm, achieved an organic combination that takes the strengths of each. With the help of MATLAB software, a BP neural network water quality evaluation model based on the hybrid optimization of quantum genetic algorithm and particle swarm optimization algorithm was established. This model solved the problems of BP neural network being prone to falling into local optimal solutions and determining the best initial state of the particle swarm optimization algorithm population. Meanwhile, the model was verified for its accuracy and feasibility through simulation experiments using the water quality data of Dongjiang Lake from 2008 to 2012.The particle swarm optimization algorithm can effectively enable the BP neural network to escape from local optimum and analyze the water quality grade from a global perspective. Moreover, the incorporation of the quantum genetic algorithm resolves the issue of initial value selection in the particle swarm optimization algorithm, maximizing its optimization capability, enhancing the evaluation accuracy and precision of the network, and better reflecting the real conditions of water bodies at different time periods. This experiment has, to a certain extent, improved the accuracy and robustness of traditional water quality evaluation, providing a stable and reliable tool for subsequent water quality-related research.更多还原