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: In this paper, an artificial neural network was used for forecasting of long-term wind speed data (24 and 48 hours ahead) in La Serena City (Chile) in order to obtain a more effective correlation and prediction, a particle swarm algorithm was implemented to update the weights of the network.
Abstract: An artificial neural network was used for forecasting of long-term wind speed data (24 and 48 hours ahead) in La Serena City (Chile). In order to obtain a more effective correlation and prediction, a particle swarm algorithm was implemented to update the weights of the network. 43800 data points of wind speed were used (years 2003- 2007), and the past values of wind speed, relative humidity, and air temperature were used as input parameters, considering that these meteorogical parameters are more readily available around the globe. Several neural network architectures were studied, and the optimum architecture was determined by adding neurons in systematic form and evaluating the root mean square error (RMSE) during the learning process. The results show that the meteorological variables used as input parameters, have influential effects on the good training and predicting capabilities of the chosen network, and that the hybrid neural network can forecast the hourly wind speed with acceptable accuracy, such as: RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 and R2 =0.97 for 24-hours-ahead wind speed prediction, and RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 for 48-hours-ahead wind speed prediction.
2 citations
01 Mar 2021
TL;DR: In this paper, a hybrid neural network model was proposed to extract more meaningful sentence semantic and structural hidden features from the word-level and sentence-level hierarchies, respectively, and pays attention to the features with a large emotional contribution rate through the attention mechanism.
Abstract: Implicit sentiment analysis is an important part of sentiment computing, especially sentiment analysis based on deep learning has become a research hotspot in recent years. In this paper, convolutional neural networks are used to extract text features, combined with long-term and short-term memory network (LSTM) structure to extract context information, and add attention mechanism to the network to construct a new hybrid neural network model to achieve implicit emotion for text analysis. The hybrid neural network model extracts more meaningful sentence semantic and structural hidden features from the word-level and sentence-level hierarchies, respectively, and pays attention to the features with a large emotional contribution rate through the attention mechanism. The classification accuracy rate of the model on the public implicit sentiment dataset has reached 77%. The research of implicit sentiment analysis can improve the effect of text sentiment analysis more comprehensively, and further promote the application of text sentiment analysis in the fields of knowledge embedding, text representation learning, user modeling and natural language.
2 citations
TL;DR: This study may provide scientific evidence and auxiliary decision support in the identification of illegal emission enterprises through boosted regression tree model and generalized extreme studentized deviate test (ESD) algorithm.
Abstract: Gao, W.; Wan, L.; Qi, S., and Wang, D., 2019. The tracing of wastewater in enterprises based on hybrid neural network. In: Hoang, A.T. and Aqeel Ashraf, M. (eds.), Research, Monitoring, an...
2 citations
2 citations
19 Apr 1993
TL;DR: In this paper, a hybrid neural network (ANN)-knowledge based system (KBS) is proposed for load forecasting of the Belgian national power system control center with a minimum lead time of 24 hours.
Abstract: The project described is aimed at automating the short-term load forecasting of the Belgian national power system control centre, usually done with a minimum lead time of 24 hours. It is hoped that the resulting system will improve the quality of forecasting methods, through a better modeling of the nonlinear relationship between load and climatic factors. In view of the various aspects of the problem, the authors intend to develop a hybrid neural network (ANN)-knowledge based system (KBS) application: the ANN will form the basis of the system and will make the forecast in normal situations; the KBS should manage exceptions and special phenomena as well as provide specific knowledge-based facilities. The authors focus on the development of a prototype for the ANN. The ANN is to be a model of the evolution of the load w.r.t. input parameters, therefore the ANN predicts the ratio between the load for one day and the day before, instead of the raw load value. >
2 citations