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.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: A novel hybrid neural network methodology is presented which also correctly classifies the unlabeled transients of the Hungarian Paks nuclear power plant simulator and has been proven as the most robust against the misleading recognition of unlabeling malfunctions.
Abstract: Proper and rapid identification of malfunctions (transients) is of premier importance for the safe operation of nuclear power plants. Feedforward neural networks trained with the backpropagation (BP) algorithm are frequently applied to model simulated nuclear power plant malfunctions. The correct identification of unlabeled transients-or transients of the "don't-know" type have proven to be especially challenging. A novel hybrid neural network methodology is presented which also correctly classifies the unlabeled transients. From this analysis the importance for properly accommodating practical aspects such as the drift of electronics elements of a simulator, the digitization of simulated and actual plant signals, and the accumulating errors during numerical integration became obvious. Beside the feedforward neural networks trained with the BP algorithm, many other types of networks and codes were used for finding the best (sensitive and robust) algorithms. Various neural network based models were successfully applied to identify labeled and unlabeled malfunctions of the Hungarian Paks nuclear power plant simulator. The BP and probabilistic methods have been proven as the most robust against the misleading recognition of unlabeled malfunctions.
76 citations
••
TL;DR: A novel EEG based computer-aided (CAD) Hybrid Neural Network that can be identified as DepHNN (Depression Hybrid Neural network) for depression screening is presented, which has attained an accuracy of 99.10% with mean absolute error (MAE) of 0.2040.
75 citations
••
01 Jul 2001
TL;DR: The hybrid diagnostic technique takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms.
Abstract: In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.
75 citations
••
TL;DR: This algorithm was implemented and applied for predicting, spatially and temporally, the hydraulic head in an area located in Bavaria, Germany and can be characterized as favorable, since the RMSE of the method is in the order of magnitude of 10−2 m.
74 citations
••
01 Apr 2004TL;DR: A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory and the general regression neural network, is proposed, able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels.
Abstract: A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.
74 citations