An improved artificial fish swarm algorithm and its application on system identification with a time-delay system
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摘要: 針對人工魚群算法(AFSA)存在收斂速度慢和尋優精度低等問題,本文提出了一種改進人工魚群算法(IAFSA).該算法中的人工魚能夠根據魚群當前狀態調整自身的視野和步長來平衡局部搜索和全局搜索.此外,算法中還加入了引導行為,即人工魚在覓食行為未發現更優的位置時,當前人工魚向最優人工魚移動一步.仿真結果表明,改進人工魚群算法在收斂速度、尋優精度和克服局部極值等方面有很大優勢.本文將改進魚群算法應用時滯系統的辨識中,辨識結果表明改進算法能獲取被控對象的精準數學模型,并具有較強的抗干擾能力.Abstract: To remedy the low convergence rate and low optimization accuracy of the artificial fish swarm algorithm (AFSA), an improved artificial fish swarm algorithm (IAFSA) was proposed. In the improved algorithm, the artificial fish could adjust the vision and step and form a balance between the local search and global search by identifying the actual condition. Furthermore, when the artificial fish in the foraging behavior does not find a better position than the current location, it steps forward to the optimal artificial fish by introducing the guide behavior to improved algorithm. The results indicate that the improved algorithm has advantages such as convergence rate, optimization accuracy, and anti local extremum value. The improved algorithm was applied to the system identification with the time-delay model. This algorithm can obtain a precise mathematical model of the controlled object and acquire great identification accuracy in the case of external interference.
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參考文獻
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