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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.


Papers
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Book ChapterDOI
01 Jan 1999
TL;DR: This concerns especially computer implementation of ANNs, called for short neurocomputing, and its applications in structural engineering and especially to mechanics of structures and materials.
Abstract: In recent 7-8 years the Artificial Neural Networks (ANNs) have been widely introduced to structural engineering and especially to mechanics of structures and materials [1]. ANNs simulate biological neural networks very primitively. This concerns especially computer implementation of ANNs, called for short neurocomputing.

3 citations

Proceedings ArticleDOI
24 May 1999
TL;DR: The obtained results have shown that the array of partially selective sensors, cooperating with hybrid neural network, can be used to determine the individual analyte concentrations in the mixture of gases with good accuracy.
Abstract: The paper presents the application of the hybrid neural network to the solution of the calibration problem of the solid state sensor array used for the gas analysis The applied neural network is composed of two parts: the selforganizing Kohonen layer and multilayer perceptron (MLP). The role of the Kohonen layer is to perform the feature extraction of the data and the MLP network fulfils the role of estimator of the concentration of the gas components. The obtained results have shown that the array of partially selective sensors, cooperating with hybrid neural network, can be used to determine the individual analyte concentrations in the mixture of gases with good accuracy. The hybrid network is a reasonably small net and thanks to this if learns faster and reaches good generalization ability at reasonably small size of training data set. The system has two interesting features: lower calibration cost and good accuracy.

3 citations

Journal Article
TL;DR: The experimental results show that the designed electronic nose system can distinguish different types of vinegar accurately, and provide a convenient method for vinegar quality evaluation.
Abstract: For non contact food quality evaluation,through hardware design scheme consists of data acquisition module and microprocessor module,an electrical noise system is designed and developed based on six kinds of Figaro metal-oxide gas sensor array for real time food non-destructive examination( NDE). As for the software design scheme,the principal component analysis( PCA) and back propagation( BP) are utilized and used to analyze gas"fingerprint information"database by LabVIEW. The experimental results show that the designed electronic nose system can distinguish different types of vinegar accurately,and provide a convenient method for vinegar quality evaluation.

2 citations

Proceedings ArticleDOI
Wen Yu1, Xiaoou Li1
06 Jul 2014
TL;DR: A hybrid neural network is proposed, which uses static and dynamic neural networks to approximate the blending properties of gasoline blending, and is provided to illustrate the neuro modeling approach.
Abstract: Gasoline blending is an important unit operation in gasoline industry. A good model for the blending system is beneficial for supervision operation, prediction of the gasoline qualities and performing model-based optimal control. Gasoline blending process involves two types of proprieties: static blending and dynamic in the blending tanks. The blending process cannot be modeled exactly, because it does not follow ideal mixing rules in practice. In this paper we propose a hybrid neural network, which uses static and dynamic neural networks to approximate the blending properties. Numerical simulations are provided to illustrate the neuro modeling approach.

2 citations


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