40 research outputs found
Obtaining Volterra Kernels from Neural Networks
No full textWorld Congress on Medical Physics and Biomedical Engineering -- AUG 27-SEP 01, 2006 -- Seoul, SOUTH KOREABoth neural networks (NN) and Volterra series (VS) are widely used in nonlinear dynamic system identification. In VS approach, the system is modeled using a set of kernel functions that correspond to different order convolutions. Kernels in VS are typically estimated using an orthogonal expansion technique. In this study, we discuss the method of obtaining VS representation of nonlinear systems from their NN models as an alternative approach and compare its modeling performances against the popular Laguerre basis expansion (LBE) technique. In LBE approach, the critical issues are to select a suitable pole parameter and number of basis functions to be used in the expansions, so that the kernels can be accurately represented. We devised novel approaches to address both issues, the pole parameter is selected using a systematic optimization approach and the number of basis functions is decided using the minimum description length criterion. Our preliminary results on synthetic data indicate that when used with these provisions, LBE yields more accurate kernels estimation results than the NN approach. However, LBE is typically used without these provisions in literature. We demonstrate that with its typical use, kernels estimated using the LBE approach can be quite misleading even though the estimation error may seem to be reasonable. Therefore, we suggest the use NN approach as a reference method to confirm the morphology of the kernels estimated via other approaches, including LBE.IUPSEM, IFMBE, IOMP, KSMP, KSMBE, AAPM, AFOMP, BMES, EFOMP, IAEA, AIMBE, IEEE EMB, WHO, SIEMENS, TomoTherapy, Philips, iba, Varian, CIVCO, Elekta, Samsun
Nonlinear system identification via Laguerre network based fuzzy systems
No full textIn this study, identification of nonlinear systems via Laguerre network based fuzzy model is introduced. We first describe the proposed modeling approach in detail and suggest a fast learning scheme for its training. The proposed approach is applied in three dynamic system modeling problems including Box-Jenkins gas furnace data and forced Van der Pol oscillator. When we compare the performance of the proposed approach against the classical Sugeno and adaptive network based fuzzy inference system modeling, our approach is found to have superior modeling performance and generalization capability. (C) 2009 Elsevier B.V. All rights reserved
Extended fuzzy function model with stable learning methods for online system identification
No full textThe aim of the online nonlinear system identification is the accurate modeling of the current local input-output behavior of the plant without using any prior knowledge and offline modeling phase. It is a challenging task for many intelligent systems when used for real-time control applications. In this paper, we propose a novel computationally efficient extended fuzzy functions (EFF) model for system identification of unknown nonlinear discrete-time systems. The main contributions are to introduce an effective quasi-nonlinear model (EFF) and propose adaptive learning rates (ALR) for recursive least squares (RLS) and gradient-descent (GD) methods. The asymptotic convergence of the modeling errors and boundedness of the parameters are proved by using the input-to-state stability (ISS) approach. Numerical simulations are performed for Box-Jenkins gas furnace system and a nonlinear dynamic system. The benefits of its accuracy, stability and simple implementation in practice indicate that EFF model is a promising technique for online identification of nonlinear systems. Copyright (C) 2010 John Wiley & Sons, Ltd
Fuzzy functions with function expansion model for nonlinear system identification
No full textIn this study, the structure of fuzzy functions is improved by function expansion. Unlike fuzzy conventional if-then rules, classical fuzzy function structure includes fuzzy bases and linear inputs. Membership functions of fuzzy bases are set using fuzzy C-means (FCM) algorithm, and the linear parameters are computed using the least-square estimation (LSE). This study has two main contributions. First, conventional "fuzzy functions" structure is powered by the expansion of orthogonal "trigonometric functions" where the approximation capabilities of the fuzzy functions are increased. Second, the widths of the normalized membership functions determined for the fuzzy function model are optimized using the Nelder-Mead simplex algorithm that provides further enhancement on the identification performance. The advantages of the proposed model are shown via offline identification of a benchmark nonlinear system and online identification of two real-time nonlinear systems
BULANlK MANTlK İLE FONKSiYON TANIMLAMA
No full textBulanık Mantık Sistemlerinin fon ksiyon tanımlama özelliği bilinmektedir. Bu çalışmada bir sinüs fonksiyonunun giriş/çıkış bilgisine karşı düşen Bulanık Mantık Sistemi iki ayrı durolayıcı ile gerçeklenmiştir. Sonuçlar sistem modellernesi açısından karşılaştınlmıştır
İki Kaplı Sıvı Seviye Kontrol Sistemi Tasarımı ve Gerçek Zamanlı Denetleyici Uygulamaları
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