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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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Book ChapterDOI
Toly Chen1
01 Jan 2007
TL;DR: A recurrent hybrid neural network is proposed in this study, in which a job is pre-classified into one category with the k-means (kM) classifier, and then the back propagation network (BPN) tailored to the category is applied to predict the completion time of the job.
Abstract: Predicting the completion time of a job is a critical task to a wafer fabrication plant (wafer fab). Many recent studies have shown that pre-classifying a job before predicting the completion time was beneficial to prediction accuracy. However, most classification approaches applied in this field could not absolutely classify jobs. Besides, whether the pre-classification approach combined with the subsequent prediction approach was suitable for the data was questionable. For tackling these problems, a recurrent hybrid neural network is proposed in this study, in which a job is pre-classified into one category with the k-means (kM) classifier, and then the back propagation network (BPN) tailored to the category is applied to predict the completion time of the job. After that, the prediction error is fed back to the kM classifier to adjust the classification result, and then the completion time of the job is predicted again. After some replications, the prediction accuracy of the hybrid kM-BPN system will be significantly improved.

1 citations

Book ChapterDOI
06 Jun 2010
TL;DR: In this work, a hybrid neural network model (HNNM) is proposed, which combines the advantages of genetic algorithm, multi-agents and reinforcement learning and Experimental results have shown to be better than those obtained by the most commonly used optimization techniques.
Abstract: In this work, a hybrid neural network model (HNNM) is proposed, which combines the advantages of genetic algorithm, multi-agents and reinforcement learning In order to generate networks with few connections and high classification performance, HNNM could dynamically prune or add hidden neurons at different stages of the training process Experimental results have shown to be better than those obtained by the most commonly used optimization techniques.

1 citations

Journal ArticleDOI
28 Jun 2014
TL;DR: The article describes the application of the artificial neural network with the authors’ hybrid training algorithm for practical problems solution in the field of medicine, namely the definition of danger level determination of tuberculosis carriers.
Abstract: In this article we investigate some approaches to the artificial neural networks training with use of hybrid algorithms. Algorithms which are based on the back propagation algorithm and the ant colony algorithm are considered in detail. The article describes the application of the artificial neural network with the authors’ hybrid training algorithm. The preliminary studies have shown that the algorithm improves the efficiency of the problems on standard test databases. The application of the algorithm for practical problems solution in the field of medicine, namely the definition of danger level determination of tuberculosis carriers is described. It was shown that the accuracy of the hybrid algorithm is up to 22% higher than of the classical one.

1 citations

Journal ArticleDOI
TL;DR: A hybrid neural network is proposed for improve the performance of the face recognition by consisted of SOM and LVQ and a comparison is made between eigenface method, hidden Markov model method, multi-layer neural network.
Abstract: The accuracy of face recognition used unmanned security system is very important and necessary. However, face recognition is a lot of restriction due to the change of distortion of face image, illumination, face size, face expression, round image. We propose a hybrid neural network for improve the performance of the face recognition. The proposed method is consisted of SOM and LVQ. In order to verify usefulness of the proposed method, we make a comparison between eigenface method, hidden Markov model method, multi-layer neural network.

1 citations


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