12 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 10 / 12
Yayın A new robust stability criterion for dynamical neural networks with multiple time delays(Elsevier Science BV, 2013-01-01) Faydasıçok, Özlem; Arik, SabriThis paper investigates the problem of the existence, uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays and parameter uncertainties. Under the assumption that the activation functions are globally Lipschitz continuous, we derive a new criterion for the robust stability of a class of delayed neural networks by utilizing the Lyapunov stability theorems and the Homomorphic mapping theorem. Different from those previously published conditions in the recent literature, the robust stability result presented in this paper not only establishes a time-independent relationship between the network parameters of the neural network, but also takes into account the number the neurons of the designed neural system. Some illustrative numerical examples are also given to make a detailed comparison between our result and the previously published corresponding results. This comparison proves that our result is new and can be considered an alternative condition to those of the previously reported robust stability results.Yayın New criteria for the existence of stable equilibrium points in nonsymmetric cellular neural networks(IEEE, 2003) Özcan, Neyir; Arık, Sabri; Tavşanoğlu, Ahmet VedatA new criteria for the existence of stable equilibrium points in nonsymmetric cellular neural networks (CNN) was presented. It was shown that the results obtained can be used to derive some complete stability conditions for some special classes of CNNs such as positive cell-linking CNNs, opposite-sign CNNs and dominant-template CNNs. The model of the CNN whose dynamical behavior was described by the state equations was discussed.Yayın On the inverse point-source problem of the poisson equation(Istanbul University, 2005) Yılmaz, Melek; Şengül, Metin; Geçkinli, MelihIn this work, a basic inverse heat conduction problem of a simple 2-D model with steady state heat source is taken into view. The physical problem is for a square region with uniform thermophysical properties and a point heat source of unit magnitude. To obtain boundary data, temperature probes are placed at the midpoints of the sides of the square domain. The objective of the inverse problem is to estimate the coordinates of the point source with the least amount of data. Initially, the inverse problem is analyzed to determine the main causes that render the problem ill conditioned. As for the solution, among the methods that has been tried so far, the best results are obtained from a backpropagating ANN with four-probe data. When white Gaussian noise is added to the measurements, no catastrophic failure has been observed.Yayın Dynamical analysis of uncertain neural networks with multiple time delays(Taylor & Francis Ltd, 2016-02-17) Arık, SabriThis paper investigates the robust stability problem for dynamical neural networks in the presence of time delays and norm-bounded parameter uncertainties with respect to the class of non-decreasing, non-linear activation functions. By employing the Lyapunov stability and homeomorphism mapping theorems together, a new delay-independent sufficient condition is obtained for the existence, uniqueness and global asymptotic stability of the equilibrium point for the delayed uncertain neural networks. The condition obtained for robust stability establishes a matrix-norm relationship between the network parameters of the neural system, which can be easily verified by using properties of the class of the positive definite matrices. Some constructive numerical examples are presented to show the applicability of the obtained result and its advantages over the previously published corresponding literature results.Yayın Derin öznitelikler ile anlambilimsel görüntü bölütleme(Institute of Electrical and Electronics Engineers Inc., 2018-07-05) Sünetci, Sercan; Ateş, Hasan FehmiDerin evrişimsel sinir ağları (ESA) pek çok sınıflandırma probleminde olduğu gibi anlambilimsel görüntü bölütlemede de çok ciddi başarı göstermiştir. Fakat derin ağların eğitilmesi hem zaman alıcıdır hem de geniş bir eğitim veri kümesine ihtiyaç duymaktadır. Bir veri kümesinde eğitilen ağın başka bir görev ya da veri kümesine uygulanabilmesi için transfer öğrenme ile yeniden eğitilmesi gerekmektedir. Transfer öğrenmeye alternatif olarak ağ katmanlarından çıkarılan öznitelik vektörleri doğrudan sınıflandırma amaçlı kullanılabilir. Bu bildiride genel ESA mimarilerinden elde edilen özniteliklerin eğitim gerektirmeyen bir görüntü etiketleme yönteminde kullanılmasının sınıflandırma başarımına katkısı incelenmiştir. Derin ağlarda ‘öğrenilmiş’ öznitelikler ile SIFT gibi ‘el yapımı’ özniteliklerin birlikte kullanılmasının etiketleme doğruluğunu artırdığı gösterilmiştir. Varolan ön eğitimli ağların kullanılması sayesinde önerilen yaklaşım herhangi bir veri kümesinde yeniden eğitime gerek olmadan kolayca uygulanabilmektedir. Önerilen yöntem iki veri kümesinde test edilmiş ve etiketleme doğruluğu benzer yöntemlerle karşılaştırmalı olarak sunulmuştur.Yayın CNN-Based deep learning architecture for electromagnetic imaging of rough surface profiles(IEEE, 2022-10) Aydın, İzde; Budak, Güven; Sefer, Ahmet; Yapar, AliA convolutional neural network (CNN) based deep learning (DL) technique for electromagnetic imaging of rough surfaces separating two dielectric media is presented. The direct scattering problem is formulated through the conventional integral equations and the synthetic scattered field data is produced by a fast numerical solution technique which is based on Method of Moments (MoM). Two different special CNN architectures are designed and implemented for the solution of the inverse rough surface imaging problem wherein both random and deterministic rough surface profiles can be imaged. It is shown by a comprehensive numerical analysis that the proposed deep-learning (DL) inversion scheme is very effective and robust.Yayın Plaka tanıma sistemi için farklı bir yaklaşım(IEEE, 2009-06-26) Tamer, Engin; Çizmeci, BurakBu bildiride, bilgisayarlı görü ve örüntü tanıma alanlarında çok popüler olan plaka tanıma sistemi için farklı bir yakla¸sım sunuyoruz. Plaka tanıma sistemi genellikle üç ana bölüme ayrılır: plakanın yerinin saptanması, karakter bölütleme ve karakter tanıma. Plaka tanıma sisteminin en önemli bölümü olan plaka yerinin saptanmasında, yatay tarama ile arama alanını daralttıktan sonra, Türk plakalarında yer alan TR işaretini kullanan yeni ve özgün bir algoritma öneriyoruz. Yeri saptanan plakanın karakterlerinin bölütlenmesi için ikili imge üzerinde morfolojik işlemler uyguluyoruz. Son olarak, karakter tanıma işleminde ise, harf ve sayı yapay sinir ağlarını ayırarak hata oranını en küçültmeyi hedefliyoruz.Yayın End-effector trajectory control in a two-link flexible manipulator through reference joint angle values modification by neural networks(Sage Publications, 2006-02) Öke, Gülay; İstefanopulos, YorgoThe basic difficulty in the control of flexible link manipulators stems from the fact that the link deflections cannot be controlled directly. Since the number of control inputs, applied by the actuators, is less than the total number of variables to be controlled, control approaches aiming at the suppression of deflections and vibrations are generally insufficient. Another possible approach is to determine new joint trajectories to minimize the error of the end-effector in the operational space. In this paper, a neural network is designed to compute incremental changes for the reference values of the joint angles to achieve successful tip tracking in the operational space. Tip position errors in the x- and y-directions are utihzed as inputs to the neural network. The cost function, which is minimized in training the neural network, is also chosen as the sum of squares of the tip position error in both directions. Joint angle control is provided by a PD controller. Simulations are carried out to evaluate the performance of the neural-network-based trajectory tracking method, and the results are depicted in both joint and operational spaces.Yayın Robust stability analysis of a class of neural networks with discrete time delays(Pergamon-Elsevier Science Ltd, 2012-05) Faydasıçok, Özlem; Arik, SabriThis paper studies the existence, uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with discrete constant time delays under parameter uncertainties. The class of the neural network considered in this paper employs the activation functions which are assumed to be continuous and slope-bounded but not required to be bounded or differentiable. We conduct a stability analysis by exploiting the stability theory of Lyapunov functionals and the theory of Homomorphic mapping to derive some easily verifiable sufficient conditions for existence, uniqueness and global asymptotic stability of the equilibrium point. The conditions obtained mainly establish some time-independent relationships between the network parameters of the neural network. We make a detailed comparison between our results and the previously published corresponding results. This comparison proves that our results are new and improve and generalize the results derived in the past literature. We also give some illustrative numerical examples to show the effectiveness and applicability of our proposed stability results.Yayın A novel method to represent speech signals(Elsevier Science BV, 2005-01) Akdeniz, Rafet; Yarman, Bekir Sıddık BinboğaIn this work, speech signals are modeled by means of the so-called pre-defined "signature functions". The pre-defined signature functions are generated using the statistical properties of the speech signals. It has been exhibited that, with a few basic signature functions, any speech signal can be generated within a tolerable error.
