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.
