با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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A novel axle temperature forecasting method based on decomposition, reinforcement learning optimization and neural network
یک روش جدید پیش بینی دمای محور بر اساس تجزیه ، بهینه سازی یادگیری تقویتی و شبکه عصبی-2020 Axle temperature forecasting technology is important for monitoring the status of the train bogie and preventing
the hot axle and other dangerous accidents. In order to achieve high-precision forecasting of axle temperature, a
hybrid axle temperature time series forecasting model based on decomposition preprocessing method, parameter
optimization method, and the Back Propagation (BP) neural network is proposed in this study. The modeling
process consists of three phases. In stage I, the empirical wavelet transform (EWT) method is used to preprocess
the original axle temperature series by decomposing them into several subseries. In stage II, the Q-learning
algorithm is used to optimize the initial weights and thresholds of the BP neural network. In stage III, the QBPNN
network is used to build the forecasting model and complete predicting all subseries. And the final
forecasting results are generated by combining all prediction results of subseries. By comparing all results over
three case predictions, it can be concluded that: (a) the proposed Q-learning based parameter optimization
method is effective in improving the accuracy of the BP neural network and works better than the traditional
population-based optimization methods; (b) the proposed hybrid axle temperature forecasting model can get
accurate prediction results in all cases and provides the best accuracy among eight general models. Keywords: Axle temperature forecasting | Hybrid model | Empirical wavelet transform | Q-learning algorithm | Parameter optimization | Q-BPNN network |
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