An improved target tracking information differentiating system using the neural network to substitute for fuzzy rules is presented for the infrared-radar dual-mode guidance system. Since the neural network training ba...An improved target tracking information differentiating system using the neural network to substitute for fuzzy rules is presented for the infrared-radar dual-mode guidance system. Since the neural network training based on the expert knowledge database is conducted off-line, the benefits for developing real-time tracking capabilities can be obtained. The network outputs the confidence degree denoted by the weight value of target information in the data fusion center according to two input variables of the measurement noise covariance and the tracking filter covariance. Simulation results show that the improved system can differentiate the target tracking information from the seeker fast and accurately.展开更多
Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagno...Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environment of the engine is harsh and the sensors will not work in high temperature, at high rotation speed, or under high pressure. Thus, there is not enough sensory data from the working engine to diagnose potential failures using existing approaches. In this paper, we consider the problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deep belief networks based on information entropy, IE-DBNs, for engine fault diagnosis. We first introduce several information entropies and propose joint complexity entropy based on single signal entropy. Second, the deep belief networks （DBNs） is analyzed and a logistic regression layer is added to the output of the DBNs. Then, information entropy is used in fault diagnosis and as the input for the DBNs. Comparison between the proposed IE-DBNs method and state-of-the-art machine learning approaches shows that the IE-DBNs method achieves higher accuracy.展开更多
文摘An improved target tracking information differentiating system using the neural network to substitute for fuzzy rules is presented for the infrared-radar dual-mode guidance system. Since the neural network training based on the expert knowledge database is conducted off-line, the benefits for developing real-time tracking capabilities can be obtained. The network outputs the confidence degree denoted by the weight value of target information in the data fusion center according to two input variables of the measurement noise covariance and the tracking filter covariance. Simulation results show that the improved system can differentiate the target tracking information from the seeker fast and accurately.
文摘Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environment of the engine is harsh and the sensors will not work in high temperature, at high rotation speed, or under high pressure. Thus, there is not enough sensory data from the working engine to diagnose potential failures using existing approaches. In this paper, we consider the problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deep belief networks based on information entropy, IE-DBNs, for engine fault diagnosis. We first introduce several information entropies and propose joint complexity entropy based on single signal entropy. Second, the deep belief networks （DBNs） is analyzed and a logistic regression layer is added to the output of the DBNs. Then, information entropy is used in fault diagnosis and as the input for the DBNs. Comparison between the proposed IE-DBNs method and state-of-the-art machine learning approaches shows that the IE-DBNs method achieves higher accuracy.
