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利用粒子群优化算法对传统的BP神经网络算法改进,建立了基于粒子群优化BP神经网络的评价模型,并将其应用到火电厂大气环境评价研究中。结合粒子群优化算法的全局寻优能力和BP神经网络算法的局部搜索优势,有效防止了网络陷入局部极小值,同时能保证评价结果的准确性。火电厂实例验证结果表明:利用粒子群优化的BP神经网络模型进行火电厂环境评价不仅计算简便,而且评价结果具有较高的可靠性。
Particle swarm optimization algorithm is used to improve the traditional BP neural network algorithm, and an evaluation model based on particle swarm optimization BP neural network is established and applied to the assessment of atmospheric environment in thermal power plants. Combining the global optimization ability of Particle Swarm Optimization (PSO) algorithm and the local search advantage of BP neural network algorithm, it can effectively prevent the network from falling into local minima and ensure the accuracy of evaluation results. The simulation results of thermal power plant show that the BP neural network model based on particle swarm optimization is not only simple and easy to evaluate, but also has high reliability.