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28 results for “Radial Basis Function Network”
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InvestigationOpen accessIn the keywordsBackpropagation Vs. Radial Basis Function Neural Model: Rainfall Intensity Classification For Flood Prediction Using Meteorology DataSoo See Chai, Wei Keat Wong, Kok Luong GohRainfall is one of the important weather variables that vary in space and time. High mean daily rainfall (>30 mm) has a high possibility of resulting in flood. Accurate prediction of this variable would save human lives and properties. Soft computing methods have been widely applied in this field. Among the various soft computing methods, Artificial Neural Network (ANN) is … …Journal of Computer Science, Vol. 12, Issue 4 · 15 Apr 2016 · 8,705 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Vehicle License Plate Detection and Recognition SystemKhalid W. MagladProblem statement: Automatic vehicle license plate detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for license plate detection and recognitions. Approach: Due to the varying characteristics of the license plate from country to country like numbering … …Journal of Computer Science, Vol. 8, Issue 3 · 7 Jan 2012 · 9,008 accessesView PDFCite
Research ArticleOpen accessIn the keywordsPrediction of Double Layer Grids' Maximum Deflection Using Neural NetworksReza Kamyab Moghadas, Kok Keong ChoongEfficient neural networks models are trained to predict the maximum deflection of two-way on two-way grids with variable geometrical parameters (span and height) as well as cross-sectional areas of the element groups. Backpropagation (BP) and Radial Basis Function (RBF) neural networks are employed for the mentioned purpose. The inputs of the neural networks are the length of the spans, L, … …American Journal of Applied Sciences, Vol. 5, Issue 11 · 30 Nov 2008 · 6,488 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Heuristic Moving Vehicle Location Prediction Technique Via Optimal Paths Selection with Aid of Genetic Algorithm and Feed Forward Back Propagation Neural NetworkE. Baby Anitha, K. DuraiswamyThe moving object or vehicle location prediction based on their spatial and temporal information is an important task in many applications. Different methods were utilized for performing the vehicle movement detection and prediction process. In such works, there is a lack of analysis in predicting the vehicles location in current as well as in future. Moreover, such methods compute the … …Journal of Computer Science, Vol. 8, Issue 12 · 12 Dec 2012 · 8,888 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Novel Approach to Head positioning using Fixed Center Interpolation Net with Weight Elimination AlgorithmMahmoud Zaki IskandaraniA reliable algorithm for head movements inside a vehicle is designed. The proposed algorithm allowed the adjustment of basic functions such as indicators, mirrors and reverse lights based on the driver final head position. The algorithm system mapped a predefined coordinates for driver’s head that resulted in a computable geometry via a sensory system which is fed to the vehicle … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,305 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAccurate Wavelet Neural Network for Efficient Controlling of an Active Magnetic Bearing SystemYoussef Harkouss, Souhad Mcheik, Roger AchkarProblem statement: The synthesis of a command by the neural network has an excellent advantage over the classical one such as PID. This study presented a fast and accurate Wavelet Neural Network (WNN) approach for efficient controlling of an Active Magnetic Bearing (AMB) system. Approach: The proposed approach combined neural network with the wavelet theory. Wavelet theory may be exploited … …Journal of Computer Science, Vol. 6, Issue 12 · 9 Nov 2010 · 15,569 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative Analysis of Neural Network Models for Predicting EUR/USD Direction: An Empirical StudyEl Badaoui Mohamed, Raouyane Brahim, El Moumen Samira, Bellafkih MostafaThis paper presents a rigorous comparative analysis of six feedforward neural network models for predicting the directional movement of the EUR/USD currency pair. The evaluated models include the Learning Vector Quantization, Cascade Neural Network, Feedforward Neural Network, Single Layer Perceptron, Multi-Layer Perceptron, and Radial Basis Function network. Utilizing daily historical data from April 2009 to May 2024, each model was … …Journal of Computer Science, Vol. 22, Issue 1 · 3 Feb 2026 · 2,330 accessesView PDFCite
Research ArticleOpen accessIn the abstractPM2.5 Prediction Using Homogenous and Heterogenous Ensemble Learning: A Comprehensive EvaluationShrabani Medhi, Minakshi GogoiAir pollution is a global issue. PM2.5 is considered to be the most dangerous pollutant. Prediction of PM2.5 concentration is important so that effective measures can be taken beforehand. A multitude of machine learning methodologies have been employed in forecasting PM2.5 levels, utilizing diverse combinations of ensemble classifiers and regressors. However, there are three important issues that need to be … …Journal of Computer Science, Vol. 20, Issue 9 · 20 Jun 2024 · 5,176 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Ensemble of Gaussian Mixture Model and Support Vector Machines for Network Intrusion DetectionOlujimi Daniel Alao, Sheriff Alimi, Shade Oluwakemi Kuyoro et al.NetworkIntrusion Detection Systems (NIDS) can protect computer networks and computersystems by detecting abnormal network packets and taking agreed action plans,such as notifying an administrator or rejecting the network packets. In this study,the aim is the implementation of NIDS with improved performance using anensemble of Support Vector Machines (SVMs) and the Gaussian Mixture Model(GMM). Four SVMs with Radial Basis Function (RBF), … …Journal of Computer Science, Vol. 18, Issue 9 · 21 Sep 2022 · 6,114 accessesView PDFCite
Research ArticleOpen accessIn the abstractEstimation of Soils Electrical Resistivity using Artificial Neural Network ApproachKpomonè Komla Apaloo-Bara, Adekunlé Akim Salami, Mawugno Koffi Kodjo et al.The knowledge of the ground electrical resistivity is essential to ensure the protection of electrical and telecommunications networks. However, the monitoring of its values is an expensive task which takes long time. Therefore, its prediction is important. This study investigates on predicting soil electrical resistivity using Artificial Neural Networks. Nine sites of our city (Lome, TOGO) were considered. After characterization … …American Journal of Applied Sciences, Vol. 16, Issue 2 · 13 Mar 2019 · 9,518 accessesView PDFCite