关键词: 量子粒子群算法, 聚合度, 收缩扩张系数, 模糊神经网络, 水质评价

Abstract: Traditional Particle Swarm Optimization（PSO） algorithm used in training the neural network has certain drawbacks such as high time-consuming, slow learning speed and easily falling into local optimum. In order to overcome these disadvantages, a new model is presented in this paper, which is generated from Takagi-Sugeno fuzzy neural network based on adaptive quantum-behaved particle. The new adaptive quantum-behaved particle swarm optimization algorithm can adaptively adjust the contraction-expansion coefficient in the iteration by introducing the concept of the aggregation degree in the algorithm, which makes the algorithm more dynamically adaptive. The new model combines the advantages of quantum-behaved particle swarm optimization with Takagi-Sugeno fuzzy neural network, and improves the generalization ability of the model. The hydrological data of Dongjiang Lake watershed stations in Hunan province of China from 2002 to 2013 are used in the experiments. The experimental results demonstrate that the proposed model is more efficient than other 4 neural network models and can be used to the daily water quality evaluation.

Key words: quantum-behaved particle swarm optimization, aggregation degree, contraction-expansion coefficient, fuzzy neural network, water quality evaluation