A damage location method using multi-layer perceptron (MLP) is developed to diagnose the cable damage of a real long span cable-stayed bridge. Firstly, the damage patterns are defined based on dynamical calculation....A damage location method using multi-layer perceptron (MLP) is developed to diagnose the cable damage of a real long span cable-stayed bridge. Firstly, the damage patterns are defined based on dynamical calculation. The analysis of damage pattern reveals that the damage patterns caused by different damage locations have inherent distinctness, while the damage extent only linearly amplifies the damage pattern curves. And 4th, 6th and 7th order frequencies are canceled from the patterns because of their insensitiveness to cable damage. Then a MLP network is designed by trail-error method to describe the 7-D mapping space of damage pattern. Identification results prove that the properly organized MLP can grasp the damage pattern and identify the damage location.展开更多
A hybrid network is presented for spatio-temporal feature detecting, which is called TS-LM-SOFM. Its top layer is a novel single layer temporal sequence recognizer called TS which can transform sparse temporal sequen...A hybrid network is presented for spatio-temporal feature detecting, which is called TS-LM-SOFM. Its top layer is a novel single layer temporal sequence recognizer called TS which can transform sparse temporal sequential pattern into abstract spatial feature representations. The bottom layer of TS-LM-SOFM, a modified self-organizing feature map, is used as a spatial feature detector. A learning matrix connects the two layers. Experiments show that the hybrid network can well capture the spatio-temporal features of input signals.展开更多
The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy sys...The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.展开更多
文摘A damage location method using multi-layer perceptron (MLP) is developed to diagnose the cable damage of a real long span cable-stayed bridge. Firstly, the damage patterns are defined based on dynamical calculation. The analysis of damage pattern reveals that the damage patterns caused by different damage locations have inherent distinctness, while the damage extent only linearly amplifies the damage pattern curves. And 4th, 6th and 7th order frequencies are canceled from the patterns because of their insensitiveness to cable damage. Then a MLP network is designed by trail-error method to describe the 7-D mapping space of damage pattern. Identification results prove that the properly organized MLP can grasp the damage pattern and identify the damage location.
文摘A hybrid network is presented for spatio-temporal feature detecting, which is called TS-LM-SOFM. Its top layer is a novel single layer temporal sequence recognizer called TS which can transform sparse temporal sequential pattern into abstract spatial feature representations. The bottom layer of TS-LM-SOFM, a modified self-organizing feature map, is used as a spatial feature detector. A learning matrix connects the two layers. Experiments show that the hybrid network can well capture the spatio-temporal features of input signals.
基金Supported by the National Natural Science Foundation of China(60074014)
文摘The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.