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: It was found that the SOM/ARIMA hybrid approach out-performs all individual ARIMA models, whilst the SOM-MLP hybrid approach achieves superior forecasting performance to all models used in this study, including three naïve models.
Abstract: This paper describes an application of hybrid neural network approaches and an assessment of the effects of missing data on highway traffic flow forecasting. Using a self-organizing map (SOM), two hybrid approaches are developed for classifying traffic into different states. In the first hybrid approach, four auto-regressive integrated moving average (ARIMA) models are included. The second approach uses two multi-layer perception (MLP) models. The effects of missing data on neural network performance when forecasting traffic flow are analyzed, and options to replace the missing data are discussed. It is concluded that overall, ARIMA models are more sensitive to the percentage of missing data than neural networks in this context.
153 citations
TL;DR: A hybrid neural network model was proposed, which integrated characteristics decomposition units, and a dynamic spline interpolation unit into the multiple ATNNs, which is quite effective in MS prediction, especially for single-factor time series.
Abstract: The availability of accurate empirical models for multi-step-ahead (MS) prediction is desirable in many areas. Some ANN technologies, such as multiple-neural network, time-delay neural network (TDNN), and adaptive time-delay neural network (ATNN), have proven successful in addressing various complicated problems. The purpose of this study was to investigate the applicability of neural network MS predictive models. Motivated by the above-mentioned technologies, we proposed a hybrid neural network model, which integrated characteristics decomposition units, and a dynamic spline interpolation unit into the multiple ATNNs. Inside the net, the regular and certain information were extracted to ATNN, while both time delays and weights were dynamically adapted. The yearly average of the sunspots, which has been considered by geophysicists, environment scientists, and climatologists as a complicated non-linear system, was selected to test the hybrid model. Comparative results were presented between a traditional MS predictive model based on TDNN and the proposed model. Validation studies indicated that the proposed model is quite effective in MS prediction, especially for single-factor time series.
140 citations
TL;DR: The superior convergence property of the parallel hybrid neural network learning algorithm presented in this paper is demonstrated.
Abstract: A new algorithm is presented for training of multilayer feedforward neural networks by integrating a genetic algorithm with an adaptive conjugate gradient neural network learning algorithm. The parallel hybrid learning algorithm has been implemented in C on an MIMD shared memory machine (Cray Y-MP8/864 supercomputer). It has been applied to two different domains, engineering design and image recognition. The performance of the algorithm has been evaluated by applying it to three examples. The superior convergence property of the parallel hybrid neural network learning algorithm presented in this paper is demonstrated. >
140 citations
TL;DR: A hybrid neural network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF–PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle volatility forecasts and a neural network fitness function for financial forecasting purposes are introduced.
140 citations
TL;DR: The results demonstrate that the ANN models can improve the forecasting performance of the GARCH models when studied in the three Latin-American markets and it is shown that the results are robust and consistent for different ANN specifications and different volatility measures.
Abstract: In this research the testing of a hybrid Neural Networks-GARCH model for volatility forecast is performed in three Latin-American stock exchange indexes from Brazil, Chile and Mexico. A detail of the methodology and application of the volatility forecast of financial series using a hybrid artificial Neural Network model are presented. The results demonstrate that the ANN models can improve the forecasting performance of the GARCH models when studied in the three Latin-American markets and it is shown that the results are robust and consistent for different ANN specifications and different volatility measures.
135 citations