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Article Citation - WoS: 13Citation - Scopus: 14Improved Global Robust Stability for Interval-Delayed Hopfield Neural Networks(Springer, 2008) Singh, VimalA modified form of a recent criterion for the global robust stability of interval-delayed Hopfield neural networks is presented. The effectiveness of the modified criterion is demonstrated with the help of an example.Letter Citation - WoS: 3Citation - Scopus: 3Global Robust Stability of Interval Delayed Neural Networks: Modified Approach(Wiley, 2009) Singh, VimalA criterion for the global robust stability of Hopfield-type delayed neural networks with the intervalized network parameters is presented. The criterion, which is derived by utilizing the idea of splitting the given interval into two intervals, is in the form of linear matrix inequality and, hence, computationally tractable. The criterion yields a less conservative condition compared with many recently reported criteria, as is demonstrated with an example. Copyright (C) 2008 John Wiley & Sons, Ltd.Article Citation - WoS: 1Citation - Scopus: 2Potential of Support-Vector Regression for Forecasting Stream Flow(Univ Osijek, Tech Fac, 2014) Radzi, Mohd Rashid Bin Mohd; Shamshirband, Shahaboddin; Aghabozorgi, Saeed; Misra, Sanjay; Akib, Shatirah; Kiah, Laiha Mat; Computer EngineeringStream flow is an important input for hydrology studies because it determines the water variability and magnitude of a river. Water resources engineering always deals with historical data and tries to estimate the forecasting records in order to give a better prediction for any water resources applications, such as designing the water potential of hydroelectric dams, estimating low flow, and maintaining the water supply. This paper presents three soft-computing approaches for dealing with these issues, i.e. artificial neural networks (ANNs), adaptive-neuro-fuzzy inference systems (ANFISs), and support vector machines (SVMs). Telom River, located in the Cameron Highlands district of Pahang, Malaysia, was used in making the estimation. The Telom River's daily mean discharge records, such as rainfall and river-level data, were used for the period of March 1984-January 2013 for training, testing, and validating the selected models. The SVM approach provided better results than ANFIS and ANNs in estimating the daily mean fluctuation of the stream's flow.Article Citation - WoS: 5Citation - Scopus: 5A Study on the Performance of Magnetic Material Identification System by Sift-Brisk and Neural Network Methods(Ieee-inst Electrical Electronics Engineers inc, 2015) Ege, Yavuz; Nazlibilek, Sedat; Kakilli, Adnan; Citak, Hakan; Kalender, Osman; Karacor, Deniz; Sengul, GokhanIndustry requires low-cost, low-power consumption, and autonomous remote sensing systems for detecting and identifying magnetic materials. Magnetic anomaly detection is one of the methods that meet these requirements. This paper aims to detect and identify magnetic materials by the use of magnetic anomalies of the Earth's magnetic field created by some buried materials. A new measurement system that can determine the images of the upper surfaces of buried magnetic materials is developed. The system consists of a platform whose position is automatically controlled in x-axis and y-axis and a KMZ51 anisotropic magneto-resistive sensor assembly with 24 sensors mounted on the platform. A new identification system based on scale-invariant feature transform (SIFT)-binary robust invariant scalable keypoints (BRISKs) as keypoint and descriptor, respectively, is developed for identification by matching the similar images of magnetic anomalies. The results are compared by the conventional principal component analysis and neural net algorithms. On the six selected samples and the combinations of these samples, 100% correct classification rates were obtained.
