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).
ردیف | عنوان | نوع |
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1 |
A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting
یک مدل یادگیری تقویتی عمیق گروه ترکیبی جدید برای پیش بینی کوتاه مدت سرعت باد-2020 Wind speed forecasting is a promising solution to improve the efficiency of energy utilization. In this
study, a novel hybrid wind speed forecasting model is proposed. The whole modeling process of the
proposed model consists of three steps. In stage I, the empirical wavelet transform method reduces the
non-stationarity of the original wind speed data by decomposing the original data into several subseries.
In stage II, three kinds of deep networks are utilized to build the forecasting model and calculate
prediction results of all sub-series, respectively. In stage III, the reinforcement learning method is
used to combine three kinds of deep networks. The forecasting results of each sub-series are combined to
obtain the final forecasting results. By comparing all the results of the predictions over three different
types of wind speed series, it can be concluded that: (a) the proposed reinforcement learning based
ensemble method is effective in integrating three kinds of deep network and works better than traditional
optimization based ensemble method; (b) the proposed ensemble deep reinforcement learning
based wind speed prediction model can get accurate results in all cases and provide the best accuracy
compared with sixteen alternative models and three state-of-the-art models. Keywords: Wind speed forecasting | Ensemble deep reinforcement learning | Empirical wavelet transform | Hybrid wind speed forecasting model |
مقاله انگلیسی |
2 |
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 |
مقاله انگلیسی |