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
By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Com...By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.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
This paper mainly discusses the selection of the technical parameters of fully mechanized top coal caving mining using the neural network technique. The comparison between computing results and experiment data shows t...This paper mainly discusses the selection of the technical parameters of fully mechanized top coal caving mining using the neural network technique. The comparison between computing results and experiment data shows that the set up neural network model has high accuracy and decision making benefit.展开更多
With the objective of establishing the necessary conditions for 3-D seismic data from a Permian plutonic oilfield in western China, we compared the technology of several multi-parameter seismic inversion methods in id...With the objective of establishing the necessary conditions for 3-D seismic data from a Permian plutonic oilfield in western China, we compared the technology of several multi-parameter seismic inversion methods in identifying igneous rocks. The most often used inversion methods are Constrained Sparse Spike Inversion (CSSI), Artificial Neural Network Inversion (ANN) and GR Pseudo-impedance Inversion. Through the application of a variety of inversion methods with log curves correction, we obtained relatively high-resolution impedance and velocity sections, effectively identifying the lithology of Permian igneous rocks and inferred lateral variation in the lithology of igneous rocks. By means of a comprehensive comparative study, we arrived at the following conclusions: the CSSI inversion has good waveform continuity, and the ANN inversion has lower resolution than the CSSI inversion. The inversion results show that multi-parameter seismic inversion methods are an effective solution to the identification of igneous rocks.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By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.
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.
基金National Natural Science Foundation of China( 5 97340 90 )
文摘This paper mainly discusses the selection of the technical parameters of fully mechanized top coal caving mining using the neural network technique. The comparison between computing results and experiment data shows that the set up neural network model has high accuracy and decision making benefit.
文摘With the objective of establishing the necessary conditions for 3-D seismic data from a Permian plutonic oilfield in western China, we compared the technology of several multi-parameter seismic inversion methods in identifying igneous rocks. The most often used inversion methods are Constrained Sparse Spike Inversion (CSSI), Artificial Neural Network Inversion (ANN) and GR Pseudo-impedance Inversion. Through the application of a variety of inversion methods with log curves correction, we obtained relatively high-resolution impedance and velocity sections, effectively identifying the lithology of Permian igneous rocks and inferred lateral variation in the lithology of igneous rocks. By means of a comprehensive comparative study, we arrived at the following conclusions: the CSSI inversion has good waveform continuity, and the ANN inversion has lower resolution than the CSSI inversion. The inversion results show that multi-parameter seismic inversion methods are an effective solution to the identification of igneous rocks.
