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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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Journal ArticleDOI
TL;DR: The inclusion of the first principles knowledge in this hybrid model is shown to improve substantially the stability of the model predictions in spite of the unmeasurability of some of the key parameters.
Abstract: A model that includes both first principles differential equations and an artificial neural network is used to forecast and control an environmental process. The inclusion of the first principles knowledge in this hybrid model is shown to improve substantially the stability of the model predictions in spite of the unmeasurability of some of the key parameters. The hybrid model estimates the unobservable parameters, and because of the constraints provided by the first principles equations, provides sensible extrapolations to the model. Thus, it can be used for process optimization as well as prediction. The hybrid model is compared with both a simple neural network with no a priori information, as well as some standard modern nonparametric statistical methods. For a variety of simulated parameter values, the hybrid model is shown to be comparable in predictive ability when used for interpolation and far superior when used for extrapolation. Copyright © 1999 John Wiley & Sons, Ltd.

7 citations

Book ChapterDOI
TL;DR: A hybrid mathematical model to describe a three-phase reactor behavior that combines neural network architecture––as a predictor block for the liquid solid mass-transfer coefficient––and phenomenological equations describing the mass-conservation principle is presented.
Abstract: Publisher Summary This chapter presents a hybrid mathematical model to describe a three-phase reactor behavior that combines neural network architecture––as a predictor block for the liquid solid mass-transfer coefficient––and phenomenological equations describing the mass-conservation principle. The optimization procedure used in the network training was based on the Fletcher–Powell algorithm. Results of the network training and validation showed the predictive capacity of the proposed model and its great potential to be used as a support for process modeling and control. To explore the potentialities of the artificial neural network (ANN), two different approaches were tested: (i) standard ANN modeling (also called “black-box”), where ANN was used to represent the whole process behavior by mapping its input to output process data and (ii) hybrid ANN modeling, where ANN was used to predict the liquid–solid mass transfer coefficient that is a parameter for the determinist model. The hybrid neural network model is composed of two blocks. The ANN block estimates a process parameter––the liquid–solid mass transfer coefficient––which is used as input to the second block, represented by the deterministic equations of the process––mass and energy balance equations.

6 citations

Journal ArticleDOI
TL;DR: In this article, a genetic algorithm is implemented to optimize the artificial neural networks used, to predict the mechanical properties of Austenitic Stainless Steel 304 (ASS-304) at elevated temperatures.

6 citations

Proceedings ArticleDOI
13 Apr 1994
TL;DR: A hybrid ANN/rule-based optimised computing architecture that utilises a simplified rule-base, based on a diphone data base, and an ANN working in parallel, results in faster learning, with no reduction in overall system performance being observed.
Abstract: Analogue neural networks (ANNs) have successfully been applied to controlling a formant speech synthesiser, resulting in high quality speech. However they are somewhat limited by the large number of hidden layer neurons needed. The paper describes the application of a hybrid ANN/rule-based optimised computing architecture to diphone speech synthesis. The architecture utilises a simplified rule-base, based on a diphone data base, and an ANN working in parallel. The number of hidden layer neurons in the ANN unit when used in parallel with the rule-base is reduced when compared to the hidden layer size of a standalone ANN used for diphone synthesis. This reduction in hidden layer size results in faster learning, with no reduction in overall system performance being observed. >

6 citations


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