A hybrid model integrating predictive capabilities of Artificial Neural Network (ANN) and optimization feature of Genetic Algorithm (GA) is developed for the purpose of inverse modeling. The proposed approach is applied to Superplastic forming of materials to predict the material properties which characterize the performance of a material. The study is carried out on two problems. For the first problem, ANN is trained to predict the strain rate sensitivity index m given the temperature and the strain rate. The performance of different gradient search methods used in training the ANN model is demonstrated. Similar approach is used for the second problem. The objective of which is to predict the input parameters, i.e. strain rate and temperature corresponding to a given flow stress value. An attempt to address one of the major drawbacks of ANN, which is the black box behavior of the model, is made by collecting information about the weights and biases used in training and formulating a mathematical expression. The results from the two problems are compared to the experimental data and validated. The results indicated proximity to the experimental data.

<p>U disertaciji su posmatrani projektivni postupci tipa konjugovanih gradijenata za re&scaron;avanje nelinearnih monotonih sistema velikih dimenzija. Ovi postupci kombinuju projektivnu metodu sa pravcima pretraživanja tipa konjugovanih gradijenata. Zbog osobine monotonosti sistema, projektivna metoda omogućava jednostavnu globalizaciju, a pravci pretraživanja tipa konjugovanih gradijenata zahtevaju malo<br />računarske memorije pa su pogodni za re&scaron;avanje sistema velikih dimenzija. Projektivni postupci tipa konjugovanih gradijenata ne koriste izvode niti funkciju cilja i zasnovani su samo na izračunavanju vrednosti funkcije sistema, pa su pogodni i za re&scaron;avanje neglatkih monotonih sistema. Po&scaron;to se globalna konvergencija dokazuje bez pretpostavki o regularnosti, ovi postupci se mogu koristiti i za re&scaron;avanje sistema sa singularnim re&scaron;enjima. U disertaciji su definisana tri nova tročlana pravca pretraživanja<br />tipa Flečer-Rivs i dva nova hibridna pravca tipa Hu-Stori. Formulisani su projektivni postupci sa novim pravcima pretraživanja i dokazana je njihova globalna konvergencija. Numeričke performanse postupaka testirane su na relevantnim primerima i poređene sa poznatim postupcima iz literature. Numerički rezultati potvrđuju da su novi postupci robusni, efikasni i uporedivi sa postojećim postupcima.</p> / <p>Projection based CG methods for solving large-scale nonlinear monotone systems are considered in this thesis. These methods combine hyperplane projection technique with conjugate gradient (CG) search directions. Hyperplane projection method is suitable for monotone systems, because it enables simply globalization, while CG directions are efficient for large-scale nonlinear systems, due to low memory. Projection based CG methods are funcion-value based, they don&rsquo;t use merit function and derivatives, and because of that they are also suitable for solving nonsmooth monotone systems. The global convergence of these methods are ensured without additional regularity assumptions, so they can be used for solving singular systems.Three new three-term search directions of Fletcher-Reeves type and two new hybrid search directions of Hu-Storey type are defined. PCG algorithm with five new CG type directions is proposed and its global convergence is established. Numerical performances of methods are tested on relevant examples from literature. These results point out that new projection based CG methods have good computational performances. They are efficient, robust and competitive with other methods.</p>

This thesis presents a study of impedance optimisation of active microstrip patch antennas to multiple frequency points. A single layered aperture coupled microstrip patch antenna has been optimised to match the source reflection coefficient of a transistor in designing an active antenna. The active aperture coupled microstrip patch antenna was optimised to satisfy Global Positioning System (GPS) frequency specifications. A rudimentary aperture coupled microstrip patch antenna consists of a rectangular antenna element etched on the top surface of two dielectric substrates. The substrates are separated by a ground plane and a microstrip feed is etched on the bottom surface. A rectangular aperture in the ground plane provides coupling between the feed and the antenna element. This type of antenna, which conveniently isolates any circuit at the feed from the antenna element, is suitable for integrated circuit design and is simple to fabricate. An active antenna design directly couples an antenna to an active device, therefore saving real estate and power. This thesis focuses on designing an aperture coupled patch antenna directly coupled to a low noise amplifier as part of the front end of a GPS receiver. In this work an in-house software package, dubbed ACP by its creator Dr Rod Waterhouse, for calculating aperture coupled microstrip patch antenna performance parameters was linked to HP-EEsof, a microwave computer aided design and simulation package by Hewlett-Packard. An ANSI C module in HP-EEsof was written to bind the two packages. This process affords the client the benefit of powerful analysis tools offered in HP-EEsof and the fast analysis of ACP for seamless system design. Moreover, the optimisation algorithms in HP-EEsof were employed to investigate which algorithms are best suited for optimising patch antennas. The active antenna design presented in this study evades an input matching network, which is accomplished by designing the antenna to represent the desired source termination of a transistor. It has been demonstrated that a dual-band microstrip patch antenna can be successfully designed to match the source reflection coefficient, avoiding the need to insert a matching network. Maximum power transfer in electrical circuits is accomplished by matching the impedance between entities, which is generally acheived with the use of a matching network. Passive matching networks employed in amplifier design generally consist of discrete components up to the low GHz frequency range or distributed elements at greater frequencies. The source termination for a low noise amplifier will greatly influence its noise, gain and linearity which is controlled by designing a suitable input matching network. Ten diverse search methods offered in HP-EEsof were used to optimise an active aperture coupled microstrip patch antenna. This study has shown that the algorithms based on the randomised search techniques and the Genetic algorithm provide the most robust performance. The optimisation results were used to design an active dual-band antenna.

The goal of this thesis is to develop nontraditional strategies to provide motion control for different engineering applications. We focus our attention on three topics: 1) roll reduction of ships in a seaway; 2) response reduction of buildings under seismic excitations; 3) new training strategies and neural-network configurations. The first topic of this research is based on a multidisciplinary simulation, which includes ship-motion simulation by means of a numerical model called LAMP, the modeling of fins and computation of the hydrodynamic forces produced by them, and a neural-network/fuzzy-logic controller. LAMP is based on a source-panel method to model the flowfield around the ship, whereas the fins are modeled by a general unsteady vortex-lattice method. The ship is considered to be a rigid body and the complete equations of motion are integrated numerically in the time domain. The motion of the ship and the complete flowfield are calculated simultaneously and interactively. The neural-network/fuzzy-logic controller can be progressively trained. The second topic is the development of a neural-network-based approach for the control of seismic structural response. To this end, a two-dimensional linear model and a hysteretic model of a multistory building are used. To control the response of the structure a tuned mass damper is located on the roof of the building. Such devices provide a good passive reduction. Once the mass damper is properly tuned, active control is added to improve the already efficient passive controller. This is achieved by means of a neural network. As part of the last topic, two new flexible and expeditious training strategies are developed to train the neural-network and fuzzy-logic controllers for both naval and civil engineering applications. The first strategy is based on a load-matching procedure, which seeks to adjust the controller in order to counteract the loads (forces and moments) which generate the motion that is to be reduced. A second training strategy provides training by means of an adaptive gradient search. This technique provides a wide flexibility in defining the parameters to be optimized. Also a novel neural-network approach called modal neural network is designed as a suitable controller for multiple-input multiple output control systems (MIMO). / Ph. D.