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37 results for “Neural network modeling”
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Research ArticleOpen accessIn the abstractA Hybrid Model of Bidirectional Long-Short Term Memory and CNN for Multivariate Time Series Classification of Remote Sensing DataSawsan Morkos GharghoryClassification of multivariate time series has got massive attention in the last decade. The traditional modeling classifiers are complicated patterns and are incompetent to capture the dependencies of multivariate time series data. To include both of the effective features and the embedded relationships in the multivariate time series, a new hybrid model which incorporates both Convolutional Neural Network (CNN) and … …Journal of Computer Science, Vol. 17, Issue 9 · 29 Sep 2021 · 6,686 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdaptive and Neural Network Based Control of Pitch of Unmanned Aerial VehiclesMackenzie T. Matthews, Sun YiThe objective is to develop a new control strategy for quadrotor types of Unmanned Aerial Systems (UAVs) that assist in closing the existing research gaps between the undesired uncertainties and current control systems. This research investigates a system modeling and the effectiveness of error elimination through adaptation in the change of the plant/system’s output. The UAV is controlled via Wi-Fi … …Journal of Mechatronics and Robotics, Vol. 3, Issue 1 · 18 Nov 2019 · 6,489 accessesView PDFCite
Research ArticleOpen accessIn the abstractChilled Water VAV System Optimization and Modeling Using Artificial Neural NetworksRand Talib, Nabil Nassif, Maya Arida, Taher Abu-LebdehIn 2016, It was estimated that about 40% of total U.S. energy consumption was consumed by the residential and commercial sectors. According to EIA, in 2009, the energy consumption in U.S. homes was 48% which was down from 58% in 1993 (Residential Energy Consumption Survey (RECS). The development of building energy savings methods and models becomes apparently more necessary for … …Energy Research Journal, Vol. 9, Issue 1 · 6 Feb 2019 · 6,449 accessesView PDFCite
Research ArticleOpen accessIn the abstractChilled Water VAV System Optimization and Modeling Using Artificial Neural NetworksRand Talib, Nabil Nassif, Maya Arida, Taher Abu-Lebdehin 2016, It was estimated that about 40% of total U.S. energy consumption was consumed by the residential and commercial sectors. According to EIA, in 2009, the energy consumption in U.S. homes was 48% which was down from 58% in 1993 Residential Energy Consumption Survey (RECS). The development of building energy savings methods and models becomes apparently more necessary for … …American Journal of Engineering and Applied Sciences, Vol. 11, Issue 4 · 23 Oct 2018 · 7,465 accessesView PDFCite
Research ArticleOpen accessIn the abstractBuilding Energy Modeling Using Artificial Neural NetworksMaya Arida, Nabil Nassif, Rand Talib, Taher Abu-LebdehAccurate modeling of total building energy is now vital to reduce energy consumption. This is especially true for buildings since they are considered as the largest energy consumer in the United States. This paper investigates modeling methods for building energy-systems using non-linear auto-regression artificial neural networks. The proposed model can forecast the whole building energy consumptions given the four input … …Energy Research Journal, Vol. 7, Issue 2 · 6 Jun 2017 · 7,125 accessesView PDFCite
Research ArticleOpen accessIn the abstractStudy of Fractional Order Integro-Differential Equations by using Chebyshev Neural NetworkSarkhosh S. Chaharborj, Shahriar S. Chaharborj, Yaghoub MahmoudiRecently fractional integro-differential equations have been proposed for the modeling of many complex systems. Simulation of these equations requires the development of suitable numerical methods. This paper is devoted to finding the numerical solution of some classes of fractional integro-differential equations by employing the Chebyshev Neural Network (ChNN). The accuracy of proposed method is shown by comparing the numerical results … …Journal of Mathematics and Statistics, Vol. 13, Issue 1 · 11 Feb 2017 · 7,403 accessesView PDFCite
Research ArticleOpen accessIn the abstractHydrological Forecasting Using Hybrid Data-Driven ApproachYoungmin Seo, Sungwon KimThis study develops a hybrid model, EEMD-FANN, coupling feed Forward Artificial Neural Network (FANN) and Ensemble Empirical Mode Decomposition (EEMD) for improving the accuracy of daily river stage forecasting. An original river stage data is broken down into a residue and Intrinsic Mode Functions (IMFs) using the EEMD and different FANNs are developed as forecasting models for the decomposed IMFs … …American Journal of Applied Sciences, Vol. 13, Issue 8 · 2 Sep 2016 · 8,136 accessesView PDFCite
Research ArticleOpen accessIn the abstractModelling the Interaction between the Antenna and the Human Head by the Technique of Artificial Neural NetworkNizar Sghaier, Lassaad Latrach, N. Sboui et al.The purpose of this study is the study and modeling of phenomenon 'interaction between the antenna and the human body' by the Artificial Neural Network (ANN). This technique is based on mathematical formulations and a base of learning who took simulations by noted trade CST MS. An example of the interaction between a body, which the dielectric properties are given … …American Journal of Engineering and Applied Sciences, Vol. 8, Issue 3 · 3 Jun 2015 · 7,422 accessesView PDFCite
EditorialOpen accessIn the abstractNew Kind of Statistical MethodsCagdas Hakan AladagAladag, C.H. and E. Egrioglu, 2012. Advanced Time Series Forecasting Methods. In: Advances in Time Series Forecasting, Aladag, C.H. and E. Egrioglu (Eds.), Bentham Science Publishers, Oak Park, ISBN-10: 1608053733. Aladag, C.H., 2011. A Hybrid Intelligent Technique Combines Neural Networks and Tabu Search Methods for Forecasting, Computer Search Algorithms. 1st Edn., Nova Publisher, ISBN: 978-1-61209-043-6. Aladag, C.H., E Egrioglu, U. … …Journal of Mathematics and Statistics, Vol. 10, Issue 4 · 28 Nov 2014 · 7,588 accessesView PDFCite
Research ArticleOpen accessIn the abstractA FRAMEWORK FOR MULTILINGUAL TEXT- INDEPENDENT SPEAKER IDENTIFICATION SYSTEMSundaradhas Selva Nidhyananthan, Ramapackiam Shantha Selva KumariThis article evaluates the performance of Extreme Learning Machine (ELM) and Gaussian Mixture Model (GMM) in the context of text independent Multi lingual speaker identification for recorded and synthesized speeches. The type and number of filters in the filter bank, number of samples in each frame of the speech signal and fusion of model scores play a vital role in … …Journal of Computer Science, Vol. 10, Issue 1 · 13 Nov 2013 · 7,274 accessesView PDFCite