| 徐 华,薛恒新,钱鹏江.基于进化学习的视觉模糊系统模型在水文预报中的应用[J].水利学报,2011,42(3): |
| 基于进化学习的视觉模糊系统模型在水文预报中的应用 |
| Application of visual fuzzy system model based on evolutional learning in hydrological forecasting |
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| DOI： |
| 中文关键词: TSK模糊系统 Weber定律 视觉原理 PSO |
| 英文关键词: TSK（Takagi-Sugeno-Kang）fuzzy system Weber law visual principle PSO |
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| 中文摘要: |
| 利用视觉TSK （Takagi-Sugeno-Kang） 模糊推理系统研究了长期水文预报问题。针对传统TSK模糊建模方法易陷入局部最小的缺陷，引入粒子群优化算法，从而建立了基于进化学习的视觉TSK模糊系统训练算法。将改进后的模型应用于2个算例的长期水文预报仿真计算。结果表明：本文模型在继承传统视觉TSK模糊建模方法优点的同时，比原模型具有更好的鲁棒性和全局收敛能力，预测结果更准确，精度更高。 |
| 英文摘要: |
| The long-term hydrological forecasting problem is studied by using the visual TSK（Takagi-Suge?no-Kang） fuzzy inference in system. In order to overcome the shortcoming of the solution may be trapped into the local minimization in the traditional visual TSK fuzzy inference training，a novel visual TSK fuzzy system model based on evolutional learning is proposed by introducing the particle swarm optimization algorithm. The new method is applied to two long-term hydrological forecasting examples. The simulation results show that the new method not only inherits the advantages of traditional visual TSK fuzzy model but also has the better global convergence and accuracy than the traditional model. |
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