基于部落粒子群优化的神经模糊推理系统预测太阳黑子数

Cheng-Hung Chen, Yen-Yun Liao, Shu-Wei Liu
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引用次数: 0

摘要

采用部落粒子群算法(TPSO)对特定神经模糊推理系统(NIS)的参数进行优化,以预测太阳黑子数。该算法采用粒子群优化(PSO)作为部落优化算法(TOA)的进化策略来平衡局部和全局搜索空间。实验结果表明,该方法收敛速度快,均方根误差小。
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Neurofuzzy inference systems based on tribal particle swarm optimization for forecasting sunspot numbers
This study presents tribal particle swarm optimization (TPSO) to optimize the parameters of the specific neurofuzzy inference system (NIS) for forecasting sunspot numbers. The proposed TPSO uses particle swarm optimization (PSO) as evolution strategies of the tribes optimization algorithm (TOA) to balance local and global exploration of the search space. Experimental results demonstrated that the proposed TPSO method converges quickly and yields a lower RMS error than other current methods.
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