Topic
Hybrid neural network
About: Hybrid neural network is a research topic. Over the lifetime, 1305 publications have been published within this topic receiving 18223 citations.
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TL;DR: A GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed, which was tested in a real-world experiment and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the model are the lowest among BPNN, GRU, and CNN forecasting methods.
Abstract: Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.
85 citations
TL;DR: A combined neural network and tabu search hybrid algorithm is proposed for solving the bilevel programming problem and the results are compared with those in the literature.
82 citations
TL;DR: In this article, an adaptive neural network is designed to approximate the system uncertainties and unknown disturbances to reduce chattering phenomena and the stability of the proposed control method is proved by Lyapunov theory.
78 citations
TL;DR: A method and system for multi-frame prediction in a hybrid neural network/hidden Markov model automatic speech recognition (ASR) system is disclosed.
Abstract: A method and system for multi-frame prediction in a hybrid neural network/hidden Markov model automatic speech recognition (ASR) system is disclosed. An audio input signal may be transformed into a time sequence of feature vectors, each corresponding to respective temporal frame of a sequence of periodic temporal frames of the audio input signal. The time sequence of feature vectors may be concurrently input to a neural network, which may process them concurrently. In particular, the neural network may concurrently determine for the time sequence of feature vectors a set of emission probabilities for a plurality of hidden Markov models of the ASR system, where the set of emission probabilities are associated with the temporal frames. The set of emission probabilities may then be concurrently applied to the hidden Markov models for determining speech content of the audio input signal.
77 citations
TL;DR: The results indicated that the PHM produces considerably better forecasts than those of LR models, and the suggested clustering approach significantly improves the forecasting results on regression analysis too.
77 citations