GPS/INS integration utilizing dynamic neural networks for vehicular navigation

Noureldin, Aboelmagd; El-Shafie, Ahmed; Bayoumi, M.M.

doi:10.1016/j.inffus.2010.01.003

Cited by 196 publications

(135 citation statements)

References 15 publications

(34 reference statements)

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“…Firstly, conventional methods (CMs) such as Auto Regression (AG), Moving Average (MA) and Auto Regression Moving Average (ARMA) have been examined for several case studies and show potential for accurate forecasting for medium stream-flow classes (Billings, 2013;Chen and Dyke, 2007;Clark et al, 2008;Husain, 1985;Kalman and others, 1960;Moradkhani et al, 2005;Noureldin et al, 2007;Schreider et al, 2001;Valipour et al, 2012;Veiga et al, 2014). However, it has been reported that 15 there are several drawbacks in developing these models (Clark et al, 2008;El-Shafie et al, 2012;Husain, 1985;Ju et al, 2009;Maier et al, 2004;Noureldin et al, 2007Noureldin et al, , 2011Schreider et al, 2001). The main meagreness that associated to the application of CMs methods for developing the forecasting model for stream-flow is the stipulation to integrate it with a pre-formulation of the trustful stochastic model to ascertain the source of uncertainty for the model input and output.…”

Section: Problem Statementmentioning

confidence: 99%

“…These models methods include Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function Neural Network (RBFNN) and Multi-Layer Perceptron Neural Network (MLPNN) with ensemble procedure (El-Shafie and Noureldin, 2011). Actually, these models revealed appropriate prospective to achieve moderately high accuracy for stream-flow forecasting especially for 15 most of the peak events at AHD.…”

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confidence: 99%

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Acclimatizing Fast Orthogonal Search (FOS) Model for River Stream-flow Forecasting

Osman

Allawi

Afan

et al. 2016

Preprint

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Abstract. River stream-flow is well-thought-out as an essential element in the hydrology studies, especially for reservoir management. Forecasting river stream-flow is the key for the hydrologists in 10 proposing certain short or long-term planning and management for water resources system. In fact, developing stream-flow forecasting models are generally categorized into two main classes; process and data-driven model. Different model techniques based on empirical methods, such as stochastic model or regression model, more recently, Artificial Intelligent (AI) models have been examined and could provide accurate stream-15 flow forecasting. However, AI models experienced crucial difficulty is the necessity to utilize appropriate pre-processing methods for the raw data. In addition, the AI model should be augmented with proper optimization model to adjust the model parameters to achieve the optimal accuracy.In this paper, a novel model namely; Fast Orthogonal Search (FOS) model is proposed to develop river stream-flow forecasting. FOS is basically structured for recognizing the difference equation and its 20 functional expression model for the mapping between the model input and output. The major advantage of using FOS is the waiver of the requirement of data pre-processing and optimization model for model parameters adjustment as these procedures are performed implicitly inside FOS. In addition, pole-zero cancellation procedure within FOS process can detect the over-fitted models and avoid them.Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016Discuss., doi:10.5194/hess- -347, 2016 Manuscript under review for journal Hydrol. Earth Syst. Sci. 2The proposed FOS method was adopted in this research to perform stream-flow forecasting model at Aswan High Dam using monthly basis for130 years. Results showed outstanding performance for streamflow forecasting accuracy compared to other AI models developed during the last 10 years.

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Section: Problem Statementmentioning

confidence: 99%

mentioning

confidence: 99%

Acclimatizing Fast Orthogonal Search (FOS) Model for River Stream-flow Forecasting

Osman

Allawi

Afan

et al. 2016

Preprint

Self Cite

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“…ML approaches have been broadly researched using soft computing engineering principles (Coppin, 2004;Ford, 1987;Gevarter, 1987;Noureldin, El-Shafie, & Bayoumi, 2011;Russell & Norvig, 2010). The most common ML models are designed by artificial intelligence (AI) techniques identify the nonlinear, dynamic, nonstationary relationships min predictor data for both regression and classification problems (Nourani, Hosseini Baghanam, Adamowski, & Kisi, 2014;Yaseen, El-shafie, Jaafar, Afan, & Sayl, 2015).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Data Intelligent Models and Applications for Water Level Prediction

Yaseen

Deo

Ebtehaj

et al. 2018

Advances in Computational Intelligence and Robotics

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“…These algorithms are used to integrate the global positioning system (GPS) and inertial navigation system (INS) measurements [16][17][18][19][20]. Such systems are based on the neural networks [16][17][18], wavelets [18,19] and cross-validations [16].…”

Section: Introductionmentioning

confidence: 99%

Data Fusion Using Fuzzy Logic Techniques Supported by Modified Weighting Factors (FLMW)

Fracczak

Podsedkowski

Kobierska

2015

Int. J. Fuzzy Syst.

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This article describes a data fusion algorithm that uses fuzzy logic techniques. The algorithm involves weighting factors, the values of which change depending on whether the conditions dictated by processes of fuzzy logic have been met. By modifying the values of weighting factors, one can achieve a measurement signal with expected properties. The article includes a general description of the algorithm and an example of its application. The algorithm was developed for the purposes of data fusion for contactless measurement of the linear and angular position of an automatic laparoscopic device or a laparoscopic camera (hereinafter Laparoscope sleeve or LS).These measurements are used in the article to close the feedback loop on the position within a servomechanism with two degrees of freedom.

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GPS/INS integration utilizing dynamic neural networks for vehicular navigation

Cited by 196 publications

References 15 publications

Acclimatizing Fast Orthogonal Search (FOS) Model for River Stream-flow Forecasting

Acclimatizing Fast Orthogonal Search (FOS) Model for River Stream-flow Forecasting

Hybrid Data Intelligent Models and Applications for Water Level Prediction

Data Fusion Using Fuzzy Logic Techniques Supported by Modified Weighting Factors (FLMW)

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