多模态控制主要采用快速切换控制方式实现,此方法在切换瞬间易引起控制器输出和系统响应出现抖动现象。为平滑过渡过程,提出了一种基于Sugeno模糊推理的控制模态切换方法,将不同控制器的控制输出作为输入引入到Sugeno系统的输出隶属函数...多模态控制主要采用快速切换控制方式实现,此方法在切换瞬间易引起控制器输出和系统响应出现抖动现象。为平滑过渡过程,提出了一种基于Sugeno模糊推理的控制模态切换方法,将不同控制器的控制输出作为输入引入到Sugeno系统的输出隶属函数,并将输出隶属函数的概念扩展以实现模态的平滑过渡。通过仿真分析基于非线性度变换比例积分微分(proportional integral differential,PID)控制和常规PID控制2种方式在静止无功补偿器上的控制效果,验证了该方法可以平滑抖动现象,实现模态切换的平稳过渡。展开更多
This paper introduces a new methodology for the damage assessment of existing-transmission structures using six layers, zero order Sugeno model. The model is a hybrid fuzzy-neural system that combines the power of neu...This paper introduces a new methodology for the damage assessment of existing-transmission structures using six layers, zero order Sugeno model. The model is a hybrid fuzzy-neural system that combines the power of neural networks and fuzzy systems. It is a learning expert system that finds the parameters of the fuzzy sets and fuzzy rules by exploiting approximation techniques from neural networks. The condition ratings of the structural components are determined based on visually observed deterioration-symptoms and the severity of those symptoms. A supervised learning process using training data and expert opinions is used to develop the expert system rules and determine the ratings of the structural components. For the learning from training data, the model uses a combination of least-square estimator and gradient descent method. A sequential least square algorithm is used to determine the weighting factors that minimized the errors. A test case is given to illustrate the power of the proposed fuzzy-neural system. It is concluded that the Sugeno model＇s ability to tune the parameters based on the training data makes it superior to the rules produced by an expert in the conventional fuzzy logic systems.展开更多
文摘多模态控制主要采用快速切换控制方式实现,此方法在切换瞬间易引起控制器输出和系统响应出现抖动现象。为平滑过渡过程,提出了一种基于Sugeno模糊推理的控制模态切换方法,将不同控制器的控制输出作为输入引入到Sugeno系统的输出隶属函数,并将输出隶属函数的概念扩展以实现模态的平滑过渡。通过仿真分析基于非线性度变换比例积分微分(proportional integral differential,PID)控制和常规PID控制2种方式在静止无功补偿器上的控制效果,验证了该方法可以平滑抖动现象,实现模态切换的平稳过渡。
文摘This paper introduces a new methodology for the damage assessment of existing-transmission structures using six layers, zero order Sugeno model. The model is a hybrid fuzzy-neural system that combines the power of neural networks and fuzzy systems. It is a learning expert system that finds the parameters of the fuzzy sets and fuzzy rules by exploiting approximation techniques from neural networks. The condition ratings of the structural components are determined based on visually observed deterioration-symptoms and the severity of those symptoms. A supervised learning process using training data and expert opinions is used to develop the expert system rules and determine the ratings of the structural components. For the learning from training data, the model uses a combination of least-square estimator and gradient descent method. A sequential least square algorithm is used to determine the weighting factors that minimized the errors. A test case is given to illustrate the power of the proposed fuzzy-neural system. It is concluded that the Sugeno model＇s ability to tune the parameters based on the training data makes it superior to the rules produced by an expert in the conventional fuzzy logic systems.
