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Article Citation - WoS: 11Citation - Scopus: 11Modified Criteria for Global Robust Stability of Interval Delayed Neural Networks(Elsevier Science inc, 2009) Singh, VimalTwo simple criteria for global robust stability of Hopfield-type interval neural networks with delay are presented. The criteria turn out to be modified versions of an earlier criterion due to Cao, Huang, and Qu. Examples show the effectiveness of the modified criteria. Numerical simulations are carried out to confirm the applicability of the modified criteria. (C) 2009 Elsevier Inc. All rights reserved.Article Citation - WoS: 15Citation - Scopus: 17Improved Global Robust Stability of Interval Delayed Neural Networks Via Split Interval: Generalizations(Elsevier Science inc, 2008) Singh, VimaldThe problem of global robust stability of Hop field-type delayed neural networks with the intervalized network parameters is revisited. Recently, a computationally tractable, i.e., linear matrix inequality (LMI) based global robust stability criterion derived from an earlier criterion based on dividing the given interval into more that two intervals has been presented. In the present paper, generalizations, i.e., division of the given interval into m intervals (where m is an integer greater than or equal to 2) is considered and some new LMI-based global robust stability criteria are derived. It is shown that, in some cases, m = 2 may not suffice, i.e., m > 2 may be needed to realize the improvement. An example showing the effectiveness of the proposed generalization is given. The paper also provides a complete and systematic explanation of the "split interval" idea. (c) 2008 Elsevier Inc. All rights reserved.Article Citation - WoS: 19Citation - Scopus: 19Identification of Materials With Magnetic Characteristics by Neural Networks(Elsevier Sci Ltd, 2012) Nazlibilek, Sedat; Ege, Yavuz; Kalender, Osman; Sensoy, Mehmet Gokhan; Karacor, Deniz; Sazh, Murat HusnuIn industry, there is a need for remote sensing and autonomous method for the identification of the ferromagnetic materials used. The system is desired to have the characteristics of improved accuracy and low power consumption. It must also autonomous and fast enough for the decision. In this work, the details of inaccurate and low power remote sensing mechanism and autonomous identification system are given. The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor with high sensitivity and low power consumption. The images and most appropriate mathematical curves and formulas for the magnetic anomalies created by the magnetic materials are obtained by 2-D motion of the sensor over the material. The contribution of the paper is the use of the images obtained by the measurement of the perpendicular component of the Earth magnetic field that is a new method for the purpose of identification of an unknown magnetic material. The identification system is based on two kinds of neural network structures. The MultiLayer Perceptron (MLP) and the Radial Basis Function (RBF) network types are used for training of the neural networks. In this work, 23 different materials such as SAE/AISI 1030, 1035, 1040, 1060, 4140 and 8260 are identified. Besides the ferromagnetic materials, three objects are also successfully identified. Two of them are anti-personal and anti-tank mines and one is an empty can box. It is shown that the identification system can also be used as a buried mine identification system. The neural networks are trained with images which are originally obtained by the remote sensing system and the system is operated by images with added Gaussian white noises. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.Article Citation - WoS: 11Citation - Scopus: 16New Lmi-Based Criteria for Global Robust Stability of Delayed Neural Networks(Elsevier Science inc, 2010) Singh, VimalSome novel, linear matrix inequality based, criteria for the uniqueness and global robust stability of the equilibrium point of Hopfield-type neural networks with delay are presented. A comparison of the present criteria with the previous criteria is made. (C) 2010 Elsevier Inc. All rights reserved.Article Citation - WoS: 16Citation - Scopus: 23A New Criterion for Global Robust Stability of Interval Delayed Neural Networks(Elsevier Science Bv, 2008) Singh, VimalA novel criterion for the global robust stability of Hopfield-type interval neural networks with delay is presented. An example showing the effectiveness of the present criterion is given. (C) 2007 Elsevier B.V. All rights reserved.
