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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01 Jan 2004
TL;DR: Based on fuzzy theory and neural network theory, a new method for power transformer fault diagnosis is introduced that can deal with uncertain factors effectively and timely, and be of high ability to synthetic fault diagnosis of power transformer.
Abstract: Based on fuzzy theory and neural network theory,a new method for power transformer fault diagnosis is introduced. According to methods of DGA, electrical experiment and environmental characteristics, a fuzzy input-based BP-ART2 model is presented. This model can deal with uncertain factors effectively and timely, and be of high ability to synthetic fault diagnosis of power transformer.
1 citations
26 Jun 2016
TL;DR: The experimental results show that the hybrid neural network model works better than the traditional text categorization method based on simple characteristics (such as CHI), and it is more suitable for extracting text semantic characteristics.
Abstract: Along with the development of social network, more and more people know the world by reading news. The problem about what kind of emotion is inspired when people read news is very worthy of discussion. This paper will mix Deep Belief Networks (DBN) model and Support Vector Machine (SVM) to a hybrid neural network model by using the Contrast Divergence (CD) algorithm to estimate the weights when training a generating model, ensure that each layer of the Restricted Boltzmann Machine (RBM) mapping the features of the inputs to the best. At the same time, we cascade the last layer of DBN and a SVM classifier to adjust judging performance. And a set of tags will be attached to the top (Associative Memory), through a process of parameter tuning, learn the identifying weights to obtain a network for the task of text classification. The experimental results show that the hybrid neural network model works better than the traditional text categorization method based on simple characteristics (such as CHI), and it is more suitable for extracting text semantic characteristics.
1 citations
Dissertation•
10 Aug 2007
TL;DR: This paper aims to provide a chronology of the events leading to and following the publication of this book and its publication in the peer-reviewed literature.
Abstract: ............................................................................................................................. iii Acknowledgments.............................................................................................................. iv Dedication ........................................................................................................................... v Table of
1 citations
Patent•
03 Sep 2019
TL;DR: In this article, a deep learning-based method for classification of resting state fMRI data is proposed, which includes the extraction of functional connectivity features, brain region comprehensive features, whole brain voxel point features and personal attribute features.
Abstract: The invention discloses a resting state fMRI data classification method and device based on deep learning. The method comprises: 1) acquiring resting state fMRI test data and performing preprocessingand obtaining tags; 2) performing brain region division on the resting state fMRI data, and extracting functional connectivity features and brain region comprehensive features; 3) extracting whole brain voxel point features; 4) extracting personal attribute features; 5) constructing a hybrid neural network model for resting state fMRI data classification; 6) processing the data for the model training part, subjecting the data as input data to mixed neural network training, and using the obtained parameters for the mixed neural network model of the resting state fMRI data classification; and 7)processing the resting state fMRI data, and inputting the obtained function connectivity features, brain region comprehensive features, whole brain voxel point features and personal attribute features into the trained hybrid neural network model for classification. The invention can retain the data form of each feature, comprehensively consider the information of each feature, and effectively improve the classification accuracy rate.
1 citations
13 Oct 2016
TL;DR: A hybrid neural network-based method is presented to predict day-ahead electricity spike prices in a deregulated electricity market along with pre-processing data mining techniques and can significantly improve the forecasting accuracy.
Abstract: A hybrid neural network-based method is presented to predict day-ahead electricity spike prices in a deregulated electricity market. First, prediction of day-ahead electricity prices is carried out by a neural network along with pre-processing data mining techniques. Second, a classifier is used to separate the forecasted prices into normal and spike prices. Third, a second neural network is trained over spike hours with selected features and is used to forecast day-ahead spike prices. Forecasted spike and normal prices are combined to produce the complete day-ahead hourly electricity price forecasting. Numerical experiments demonstrate that the proposed method can significantly improve the forecasting accuracy.
1 citations