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基于关联模糊神经网络和改进型蜂群算法的负荷预测方法 被引量:16
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作者 赵芝璞 高超 +1 位作者 沈艳霞 陈杰 《中国电力》 CSCD 北大核心 2018年第2期54-60,共7页
为提高负荷预测精度,考虑历史负荷数据之间相关联的特性,利用关联模糊神经网络建立了负荷预测模型。与其他负荷预测方法相比,基于关联模糊神经网络和改进型蜂群算法的负荷预测方法,减少了模型所需要的模糊规则的数量,降低了模型的复杂... 为提高负荷预测精度,考虑历史负荷数据之间相关联的特性,利用关联模糊神经网络建立了负荷预测模型。与其他负荷预测方法相比,基于关联模糊神经网络和改进型蜂群算法的负荷预测方法,减少了模型所需要的模糊规则的数量,降低了模型的复杂度。将该方法应用于某地实际负荷预测,数值结果表明,该方法具有较高的预测精度。 展开更多
关键词 电力系统 负荷预测 关联模糊神经网络 改进型蜂群算法 负荷历史数据
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Decentralized Control Based on FNNSMC for Interconnected Uncertain Nonlinear Systems
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作者 达飞鹏 宋文忠 《Journal of Southeast University(English Edition)》 EI CAS 1998年第2期86-92,共7页
A new type controller, fuzzy neural networks sliding mode controller (FNNSMC), is developed for a class of large scale systems with unknown bounds of high order interconnections and disturbances. Although sliding mod... A new type controller, fuzzy neural networks sliding mode controller (FNNSMC), is developed for a class of large scale systems with unknown bounds of high order interconnections and disturbances. Although sliding mode control is simple and insensitive to uncertainties and disturbances, there are two main problems in the sliding mode controller (SMC): control input chattering and the assumption of known bounds of uncertainties and disturbances. The FNNSMC, which incorporates the fuzzy neural networks (FNN) and the SMC, can eliminate the chattering by using the continuous output of the FNN to replace the discontinuous sign term in the SMC. The bounds of uncertainties and disturbances are also not required in the FNNSMC design. The simulation results show that the FNNSMC has more robustness than the SMC. 展开更多
关键词 sliding mode control fuzzy neural networks interconnected nonlinear systems
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