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.

City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN

Abstract: Traffic congestion is a significant problem faced by large and growing cities that hurt the economy, commuters, and the environment. Forecasting the congestion level of a road network timely can prevent its formation and increase the efficiency and capacity of the road network. However, despite its importance, traffic congestion prediction is not a hot topic among the researcher and traffic engineers. It is due to the lack of high-quality city-wide traffic data and computationally efficient algorithms for traffic prediction. In this paper, we propose (i) an efficient and inexpensive city-wide data acquisition scheme by taking a snapshot of traffic congestion map from an open-source online web service; Seoul Transportation Operation and Information Service (TOPIS), and (ii) a hybrid neural network architecture formed by combing Convolutional Neural Network, Long Short-Term Memory, and Transpose Convolutional Neural Network to extract the spatial and temporal information from the input image to predict the network-wide congestion level. Our experiment shows that the proposed model can efficiently and effectively learn both spatial and temporal relationships for traffic congestion prediction. Our model outperforms two other deep neural networks (Auto-encoder and ConvLSTM) in terms of computational efficiency and prediction performance.

Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors

Abstract: A hybrid model is analyzed to predict the price volatility of gold, silver and copperThe hybrid model used is a ANN-GARCH model with regressors.APGARCH with exogenous variables is used as benchmark.The benchmark is better than the classical GARCH used in previous studies.The incorporation of ANN into the best Garch with regressors increases the accuracy. In this article, we analyze volatility forecasts associated with the price of gold, silver, and copper, three of the most important metals in the world market. First, a group of GARCH models are used to forecast volatility, including explanatory variables like the US Dollar-Euro and US Dollar-Yen exchange rates, the oil price, and the Chinese, Indian, British, and American stock market indexes. Subsequently, these model predictions are used as inputs for a neural network in order to analyze the increase in hybrid predictive power. The results obtained show that for these three metals, using the hybrid neural network model increases the forecasting power of out-of-sample volatility. In order to optimize the results, we conducted a series of sensitizations of the artificial neural network architecture and analyses for different cases, finding that the best models to forecast the price return volatility of these main metals are the ANN-GARCH model with regressors. Due to the heteroscedasticity in the financial series, the loss function used is Heteroskedasticity-adjusted Mean Squared Error (HMSE), and to test the superiority of the models, the Model Confidence Set is used.

Human Action Recognition Using a Modified Convolutional Neural Network

Abstract: In this paper, a human action recognition method using a hybrid neural network is presented. The method consists of three stages: preprocessing, feature extraction, and pattern classification. For feature extraction, we propose a modified convolutional neural network (CNN) which has a three-dimensional receptive field. The CNN generates a set of feature maps from the action descriptors which are derived from a spatiotemporal volume. A weighted fuzzy min-max (WFMM) neural network is used for the pattern classification stage. We introduce a feature selection technique using the WFMM model to reduce the dimensionality of the feature space. Two kinds of relevance factors between features and pattern classes are defined to analyze the salient features.

A hybrid neural network system for pattern classification tasks with missing features

Abstract: A hybrid neural network comprising fuzzy ARTMAP and fuzzy c-means clustering is proposed for pattern classification with incomplete training and test data. Two benchmark problems and a real medical pattern classification tasks are employed to evaluate the effectiveness of the hybrid network. The results are analyzed and compared with those from other methods.

A 3.43TOPS/W 48.9pJ/pixel 50.1nJ/classification 512 analog neuron sparse coding neural network with on-chip learning and classification in 40nm CMOS

Abstract: A digital-analog hybrid neural network exploits efficient analog computation and digital intra-network communication for feature extraction and classification. Taking advantage of the inherently low SNR requirements of the Locally Competitive Algorithm (LCA), the internally-analog neuron is 3x smaller and 7.5x more energy efficient than an equivalent digital design. This work demonstrates large-scale integration of 512 analog neurons using a traditional scalable digital workflow to achieve a best-of-class power efficiency of 3.43TOPS/W for object classification. At 48.9pJ/pixel and 50.1nJ/classification, the prototype 512-neuron IC achieves 2x efficiency over the digital design while maintaining reliable classification results over PVT.