Structured neural networks for modeling and identification of nonlinear mechanical systems

Kılıç, Ergin
Most engineering systems are highly nonlinear in nature and thus one could not develop efficient mathematical models for these systems. Artificial neural networks, which are used in estimation, filtering, identification and control in technical literature, are considered as universal modeling and functional approximation tools. Unfortunately, developing a well trained monolithic type neural network (with many free parameters/weights) is known to be a daunting task since the process of loading a specific pattern (functional relationship) onto a generic neural network is proven to be a NP-complete problem. It implies that if training is conducted on a deterministic computer, the time required for training process grows exponentially with increasing size of the free parameter space (and the training data in correlation). As an alternative modeling technique for nonlinear dynamic systems; this thesis proposed a general methodology for structured neural network topologies and their corresponding applications are realized. The main idea behind this (rather classic) divide-and-conquer approach is to employ a priori information on the process to divide the problem into its fundamental components. Hence, a number of smaller neural networks could be designed to tackle with these elementary mapping problems. Then, all these networks are combined to yield a tailored structured neural network for the purpose of modeling the dynamic system under study accurately. Finally, implementations of the devised networks are taken into consideration and the efficiency of the proposed methodology is tested on four different types of mechanical systems.


Prediction of Nonlinear Drift Demands for Buildings with Recurrent Neural Networks
Kocamaz, Korhan; Binici, Barış; Tuncay, Kağan (2021-09-08)
Application of deep learning algorithms to the problems of structural engineering is an emerging research field. Inthis study, a deep learning algorithm, namely recurrent neural network (RNN), is applied to tackle a problemrelated to the assessment of reinforced concrete buildings. Inter-storey drift ratio profile of a structure is a quiteimportant parameter while conducting assessment procedures. In general, procedures require a series of timeconsuming nonlinear dynamic analysis. In this study, an extensiv...
Investigation of decoupling techniques for linear and nonlinear systems
Kalaycıoğlu, Taner; Özgüven, Hasan Nevzat; Department of Mechanical Engineering (2018)
Structural coupling methods are widely used in predicting dynamics of coupled systems. In this study, the reverse problem, i.e. predicting the dynamic behavior of a particular subsystem from the knowledge of the dynamics of the overall system and of all the other subsystems, is studied. This problem arises when a substructure cannot be measured separately, but only when coupled to neighboring substructures. The dynamic decoupling problem of coupled linear structures is well investigated in literature. Howev...
Comparison of iterative algorithms for parameter estimation in nonlinear regression
Musluoğlu, Gamze; Akkaya, Ayşen; Department of Statistics (2018)
Nonlinear regression models are more common as compared to linear ones for real life cases e.g. climatology, biology, earthquake engineering, economics etc. However, nonlinear regression models are much more complex to fit and to interpret. Classical parameter estimation methods such as least squares and maximum likelihood can also be adopted to fit the model in nonlinear regression as well, but explicit solutions can not be achieved unlike linear models. At this point, iterative algorithms are utilized to ...
Parallel solution of sparse triangular linear systems on multicore platforms
Çuğu, İlke; Manguoğlu, Murat; Department of Computer Engineering (2018)
Many large-scale applications in science and engineering require the solution of sparse linear systems. One well-known approach is to solve these systems by factorizing the coefficient matrix into nonsingular sparse triangular matrices and solving the resulting sparse triangular systems via backward and forward sweep (substitution) operations. This can be considered as a direct solver or it is part of the preconditioning operation in an iterative scheme if incomplete factorization is computed. Often, these ...
Nonlinear Structural Coupling: Experimental Application
Kalaycioglu, Taner; Özgüven, Hasan Nevzat (2014-02-06)
In this work, the nonlinear structural modification/coupling technique proposed recently by the authors is applied to a test system in order to study the applicability of the method to real structures. The technique is based on calculating the frequency response functions of a modified system from those of the original system and the dynamic stiffness matrix of the nonlinear modifying part. The modification can also be in the form of coupling a nonlinear system to the original system. The test system used i...
Citation Formats
E. Kılıç, “Structured neural networks for modeling and identification of nonlinear mechanical systems,” Ph.D. - Doctoral Program, Middle East Technical University, 2012.