Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/433
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dc.contributor.supervisorSharma, R.K.-
dc.contributor.supervisorChakraverty, S.-
dc.contributor.supervisorSharma, G.K.-
dc.contributor.authorSingh, Varinder Pal-
dc.date.accessioned2007-10-01T12:28:53Z-
dc.date.available2007-10-01T12:28:53Z-
dc.date.issued2007-05-04-
dc.identifier.urihttp://hdl.handle.net/123456789/433-
dc.description.abstractThis present investigation deals with the ability of soft computing techniques, in particular, artificial neural networks in solving vibration and system identification problems. Artificial neural networks are also called parallel distributed systems because they are composed of a series of interconnected processing elements that operate in parallel. New training algorithms have been developed to train artificial neural networks and those are applied to the vibration analysis of structural members as well as system identification problems of structural dynamics for partially known and completely unknown systems. The structural problem is continually acquiring greater importance in modern science and technology and being solved with the help of different analytical and approximate methods. Structural members are often encountered in several engineering applications and their use in machine design, aeronautical engineering, nuclear reactor technology, naval structures, and earthquake resistance structures are very common. The system identification problem is an area of importance in structural engineering and is used to improve dynamic modeling capabilities for civil infrastructure systems such as high-rise building, bridges and dams. The main contributions of this study are the following. • New artificial neural network learning algorithms developed which make use of the coefficients of linear and nonlinear regression polynomials, including single and multiple variables, as training weights. These polynomials depend upon the ANN architecture to be considered for a particular problem. The proposed algorithm is henceforth abbreviated as RBNN (Regression Based Neural Network). In these models, the number of neurons in the hidden layer may be fixed depending upon the required polynomial degree. • This RBNN model is applied to estimate the vibration characteristics for free vibration of elastic plates with different boundary conditions at the edges. • The proposed algorithm is also applied to partially known and partially unknown system identification problems. Both multi-input single-output and multi-input multi-output cases have been considered for the analysis and simulation. The whole range of the subject in this thesis is covered in six chapters, which deal with brief discussion of the existing soft computing techniques, vibration and system identification studies and the proposed new algorithms. Then the thesis investigates the xiv application of these techniques to continuous and discrete systems in terms of vibration and inverse vibration analysis. The reliability, efficiency and powerfulness of the proposed techniques are also discussed by comparing the present with known results in special cases.en
dc.format.extent3269130 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen_USen
dc.publisherTUen
dc.subjectDistributed Environmenten
dc.subjectSoft Computing Techniquesen
dc.titleSoft Computing Techniques in a Distributed Environmenten
dc.typeThesisen
Appears in Collections:Ideas Unlimited @ TIET University
Doctoral Theses@CSED

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