A globally convergent algorithm for training multilayer perceptrons for data classification and interpolation

dc.contributor.advisorAazhang, Behnaamen_US
dc.creatorMadyastha, Raghavendra K.en_US
dc.date.accessioned2009-06-04T00:22:01Zen_US
dc.date.available2009-06-04T00:22:01Zen_US
dc.date.issued1991en_US
dc.description.abstractThis thesis addresses the issue of applying a "globally" convergent optimization scheme to the training of multi-layer perceptrons, a class of Artificial Neural Networks, for the detection and classification of signals in single- and multi-user communication systems. The research is motivated by the fact that a multi-layer perceptron is theoretically capable of approximating any nonlinear function to within any specified accuracy. The object function to which we apply the optimization algorithm is the error function of the multilayer perceptron, i.e., the average of the sum of the squares of the differences between the actual and the desired outputs to specified inputs. Until recently, the most widely used training algorithm has been the Backward Error Propagation algorithm, which is based on the algorithm for "steepest descent" and hence, is at best linearly convergent. The algorithm discussed here combines the merits of two well known "global" algorithms--the Conjugate Gradients and the Trust Region algorithms. A further technique known as preconditioning is used to speed up the convergence by clustering the eigenvalues of the "effective Hessian". The Preconditioned Conjugate Gradients--Trust Regions algorithm is found to be superlinearly convergent and hence, outperforms the standard backpropagation routine.en_US
dc.format.extent103 p.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.callnoThesis E.E. 1991 Madyasthaen_US
dc.identifier.citationMadyastha, Raghavendra K.. "A globally convergent algorithm for training multilayer perceptrons for data classification and interpolation." (1991) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/13532">https://hdl.handle.net/1911/13532</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/13532en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectElectronicsen_US
dc.subjectElectrical engineeringen_US
dc.subjectArtificial intelligenceen_US
dc.titleA globally convergent algorithm for training multilayer perceptrons for data classification and interpolationen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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