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Turbine through-flow model is the great part of the performance analysis model and fault diagnose. However, the performance analysis and fault diagnosis in power plants is depend on plenty of thermal parameters of which the reliability and accuracy are very important for real-time monitoring and automatically control. The results of traditional models would be impacted by the inaccurate parameters...
In this paper, an adaptive generalized predictive control method using adaptive-network-based fuzzy-inference system (ANFIS) and multiple models is proposed for a class of uncertain discrete-time nonlinear systems with unstable zero-dynamics. The proposed controller consists of a linear and robust generalized predictive adaptive controller, a nonlinear generalized predictive adaptive controller based...
In this paper, an on-line signature verification system exploiting local and global information using two-stage fusion is presented. At the first stage, global information is extracted as 13-dimensional vector and recognized by majority classifiers, and then local information is extracted as time functions of various dynamic properties and recognized by BP neural network classifier. By fusing global...
This paper proposes an audio information hiding algorithm based on BP neural network. The algorithm adopts 256 grey scales image as embedded information and audio as the carrier. Utilize learning samples to train the neural network, and then extract image information by the trained network. The advantages of this algorithm are as follows: a great deal of data can be hided, the time of extracting data...
This paper presents artificial neural networks (ANNs) for the criticality class evaluating of spare parts in a power plant. Two learning methods are utilized in the ANNs, namely back propagation (BP) and BP-particle swarm optimization (BP-PSO). The reliability of the models is tested by comparing their classification ability with a hold-out sample and an external data set. The results show that both...
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