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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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TL;DR: In this article, the Gamma and M-tests (GT) approach was combined with the hybrid WANN model for forecasting spatio-temporal groundwater fluctuations in a complex alluvial aquifer system.
Abstract: The proper design, development, and appropriate tuning of the Hybrid Neural Network architecture, mainly for its parsimoniousity and optimal training can help practitioners to generate a robust predictive tool for modeling several important hydrological processes within the water resources sector. In this paper, the Feedforward Artificial Neural Network (FFANN) and the hybrid WANN model have been developed, and later, coupled with the Gamma and M-tests (GT) approach for forecasting spatio-temporal groundwater fluctuations in a complex alluvial aquifer system. The performance of these hybrid models were evaluated using goodness-of-fit criteria. An analysis of the modeling results indicates that the GT coupled with the WANN model was able to provide significantly improved results, with lower values of the root mean square error (RMSE) and higher values of the NSE metric for the 1-week and 3-week lead times. Hence, utilizing this hybrid model, the groundwater level prediction tests were extended for 6-week and 12-week lead times with the GT approach, coupled with the WANN hybrid model only. The results showed that the accuracy of the GT-WANN hybrid model was better for the unconfined aquifer system compared to the leaky confined aquifer system. Furthermore, the present study also examined the interdependence between different model inputs and output variables for the selected study sites by means of the Wavelet Coherent Analysis (WCA). These results indicated that all the model’s input variables have a significant effect on the groundwater level of unconfined aquifers, and confirmed the nature of the aquifers tapped within the present study sites. The study finally concludes that the GT-WANN approach can be a robust predictive tool for modeling spatio-temporal fluctuations of groundwater levels.

30 citations

Journal ArticleDOI
TL;DR: A hybrid neural network, which combines the sigmoid neurons and the radial basis function neurons at the hidden layer, is proposed to better map the input-Output relationship both locally and globally.

30 citations

01 Jan 2002
TL;DR: In this paper, a modular hybrid neural network architecture called SHAME for emotion learning is introduced, which learns from annotated data how the emotional state is generated and changes due to internal and external stimuli.
Abstract: A modular hybrid neural network architecture, called SHAME, for emotion learning is introduced. The system learns from annotated data how the emotional state is generated and changes due to internal and external stimuli. Part of the modular architecture is domain independent and part must be adapted to the domain under consideration. The generation and learning of emotions is based on the event appraisal model. The architecture is implemented in a prototype consisting of agents trying to survive in a virtual world. An evaluation of this prototype shows that the architecture is capable of generating natural emotions and furthermore that training of the neural network modules in the architecture is computationally feasible. Keywords: hybrid neural systems, emotions, learning, agents.

29 citations

Journal ArticleDOI
TL;DR: A hybrid neural network with a cost-sensitive support vector machine (hybrid NN-CSSVM) with complementary advantages of two architectures for class-imbalanced multimodal data performs excellently, even with data having a minor-class proportion of only 2%.

29 citations

Journal ArticleDOI
TL;DR: In this article, two different evolutionary algorithm-based neural network models were developed to optimise the unit production cost, namely, GA-NN and PSO-NN, for machining glass fiber-reinforced plastic (GFRP) composite.
Abstract: In this paper, two different evolutionary algorithm-based neural network models were developed to optimise the unit production cost. The hybrid neural network models are, namely, genetic algorithm-based neural network (GA-NN) model and particle swarm optimization- based neural network (PSO-NN) model. These hybrid neural network models were used to find the optimal cutting conditions of Ti(C,N) mixed alumina-based ceramic cutting tool (CC650) and SiC whisker-reinforced alumina- based ceramic cutting tool (CC670) on machining glass fibre-reinforced plastic (GFRP) composite. The objective considered was the minimization of unit production cost subjected to various machine constraints. An orthogonal design and analysis of variance was employed to determine the effective cutting parameters on the tool life. Neural network helps obtain a fairly accurate prediction, even when enough and adequate information is not available. The GA-NN and PSO-NN models were compared for their performance. Optimal cutting conditions obtained with the PSO-NN model are the best possible compromise com- pared with the GA-NN model during machining GFRP composite using alumina cutting tool. This model also proved that neural networks are capable of reducing uncertainties related to the optimization and estimation of unit production cost.

29 citations


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