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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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Journal ArticleDOI
TL;DR: A novel hybrid neural network model architecture (LSCNN) was proposed with the data augmentation technology, which is can outperforms many single neural network models and enhances the generalization ability of the proposed model.
Abstract: As for the complexity of language structure, the semantic structure, and the relative scarcity of labeled data and context information, sentiment analysis has been regarded as a challenging task in Natural Language Processing especially in the field of short-text processing. Deep learning model need a large scale of training data to overcome data sparseness and the over-fitting problem, we propose multi-granularity text-oriented data augmentation technologies to generate large-scale artificial data for training model, which is compared with Generative adversarial network(GAN). In this paper, a novel hybrid neural network model architecture(LSCNN) was proposed with our data augmentation technology, which is can outperforms many single neural network models. The proposed data augmentation method enhances the generalization ability of the proposed model. Experiment results show that the proposed data augmentation method in combination with the neural networks model can achieve astonishing performance without any handcrafted features on sentiment analysis or short text classification. It was validated on a Chinese on-line comment dataset and Chinese news headline corpus, and outperforms many state-of-the-art models. Evidence shows that the proposed data argumentation technology can obtain more accurate distribution representation from data for deep learning, which improves the generalization characteristics of the extracted features. The combination of the data argumentation technology and LSCNN fusion model is well suited to short text sentiment analysis, especially on small scale corpus.

39 citations

DatasetDOI
01 Jan 2014
TL;DR: This paper presents an effective method that can be used to predict the expected bus arrival time at individual bus stops along a service route that combines a neural network that infers decision rules from historical data with Kalman filter that fuses prediction calculations with current GPS measurements.
Abstract: The ability to obtain accurate predictions of bus arrival time on a real time basis is vital to both bus operations control and passenger information systems. Several studies have been devoted to this arrival time prediction problem in many countries; however, few resulted in completely satisfactory algorithms. This paper presents an effective method that can be used to predict the expected bus arrival time at individual bus stops along a service route. This method is a hybrid scheme that combines a neural network (NN) that infers decision rules from historical data with Kalman filter (KF) that fuses prediction calculations with current GPS measurements. The proposed algorithm relies on real-time location data and takes into account historical travel times as well as temporal and spatial variations of traffic conditions. A case study on a real bus route is conducted to evaluate the performance of the proposed algorithm in terms of prediction accuracy. The results indicate that the system is capable of achieving satisfactory performance and accuracy in predicting bus arrival times for Egyptian environments.

39 citations

Journal ArticleDOI
10 Dec 2020-Energies
TL;DR: A five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico, showing that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model,the Lasso regression or the Ridge regression.
Abstract: Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent results. In this paper, a five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico. In the proposed hybrid model, the convolutional layer acts like a filter, extracting local features of the data; then the temporal features are extracted by the long short-term memory network. Finally, the performance of the hybrid model with five layers is compared with a single model (a single LSTM), a CNN-LSTM hybrid model with two layers and two well known popular benchmarks. The results also shows that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model, the Lasso regression or the Ridge regression.

39 citations

Journal ArticleDOI
TL;DR: The accuracy of the developed network has been tested by predicting the injection pressure and injection time for few engineering components and found that the overall error is 0.93% with a deviation of 3.93%.

39 citations

Journal ArticleDOI
TL;DR: This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement an online option price predictor and found that the new hybrid model is superior to pure SVM models or hybrid neural network models in terms of three types of options.
Abstract: This study develops a hybrid model that combines unscented Kalman filters (UKFs) and support vector machines (SVMs) to implement an online option price predictor. In the hybrid model, the UKF is used to infer latent variables and make a prediction based on the Black-Scholes formula, while the SVM is employed to model the nonlinear residuals between the actual option prices and the UKF predictions. Taking option data traded in Taiwan Futures Exchange, this study examined the forecasting accuracy of the proposed model, and found that the new hybrid model is superior to pure SVM models or hybrid neural network models in terms of three types of options. This model can help investors for reducing their risk in online trading.

38 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20233
20228
2021128
2020119
2019104
201863