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This paper investigates the performance of conjugate gradient algorithms with sliding-window approach for training multilayer perceptron (MLP). Online learning is implemented when the system under investigation is time varying or when it is not convenient to obtain a full history of offline data about the system variables. Sliding window framework is proposed to combine the robustness of offline learning...
Geometric form of Ni60 alloy fabricated by laser cladding is analyzed by a new diagnosis algorithm in which Back-propagation is combined with fuzzy classifier. In some complexity environments, the classic BP neural network appears limitation in classification. The proposed approach in this paper uses fuzzy model to the neural network structure. It considers classify variance and energy function to...
The quality detection of the cold-strip steel using artificial neural networks is studied. A simple Back-propagation (BP) algorithm based on error function was presented. It deals with the saturation areas that play a significant role in the slow convergence of standard BP algorithm. A modified error function was constructed to make the weight adjustment to avoid falling into the saturation areas...
Neural networks (NNs) have been widely used to predict financial distress because of their excellent performances of treating non-linear data with self-learning capability. However, the shortcoming of NNs is also significant due to a ldquoblack boxrdquo syndrome. Moreover, in many situations NNs more or less suffer from the slow convergence and occasionally involve in a local optimal solution, which...
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