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: This work proposes a highly accurate hybrid method for the diagnosis of coronary artery disease that is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network.
343 citations
TL;DR: Simulations show that the SOFNN has the capability to encode fuzzy rules in the resulting network, based on new adding and pruning techniques and a recursive learning algorithm.
253 citations
01 Sep 1996
TL;DR: Empirical results using Korean bankruptcy data show that hybrid neural network model models are very promising neural network models for bankruptcy prediction in terms of predictive accuracy and adaptability.
Abstract: The objective of this paper is to develop the hybrid neural network models for bankruptcy prediction. The proposed hybrid neural network models are (1) a MDA-assisted neural network, (2) an ID3-assisted neural network, and (3) a SOFM(self organizing feature map)-assisted neural network. Both the MDA-assisted neural network and the 11)3-assisted neural network are the neural network models operating with the input variables selected by the MDA method and 1133 respectively. The SOFM-assisted neural network combines a backpropagation model (supervised learning) with a SOFM model (unsupervised learning). The performance of the hybrid neural network model is evaluated using MDA and ID3 as a benchmark. Empirical results using Korean bankruptcy data show that hybrid neural network models are very promising neural network models for bankruptcy prediction in terms of predictive accuracy and adaptability.
249 citations
TL;DR: Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 96% by using the hybrid structure.
217 citations
TL;DR: In this article, a wind power forecasting strategy composed of a feature selection component and a forecasting engine is proposed, which applies an irrelevancy filter and a redundancy filter to the set of candidate inputs.
Abstract: Following the growing share of wind energy in electric power systems, several wind power forecasting techniques have been reported in the literature in recent years. In this paper, a wind power forecasting strategy composed of a feature selection component and a forecasting engine is proposed. The feature selection component applies an irrelevancy filter and a redundancy filter to the set of candidate inputs. The forecasting engine includes a new enhanced particle swarm optimization component and a hybrid neural network. The proposed wind power forecasting strategy is applied to real-life data from wind power producers in Alberta, Canada and Oklahoma, U.S. The presented numerical results demonstrate the efficiency of the proposed strategy, compared to some other existing wind power forecasting methods.
202 citations