1,720,968 research outputs found
Calibrating artificial neural networks by global optimization
Get PDFÖzyeğin University Technical ReportAn artificial neural network (ANN) is a computational model − implemented as a computer program − that is aimed at emulating the key features and operations of biological neural networks. ANNs are extensively used to model unknown or unspecified functional relationships between the input and output of a “black box” system. In order to apply such a generic procedure to actual decision problems, a key requirement isANN training to minimize the discrepancy between modeled and measured system output. In this work, we consider ANN training as a (potentially) multi-modal optimization problem. To address this issue, we introduce a global optimization (GO) framework and corresponding GO software. The practical viability of the GO based approach is illustrated by finding close numerical approximations of (one-dimensional, but non-trivial) functions
Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.In many real-world applications of optimization, the underlying descriptive system model is defined by computationally expensive functions: simulation modules, numerical models and other “black box” model components are typical examples. In such cases, the model development and optimization team often has to rely on optimization carried out under severe resource constraints. To address this important issue, recently a Regularly Spaced Sampling (RSS) module has been added to the Lipschitz Global Optimizer (LGO) solver suite. RSS generates non-collapsing space filling designs, and produces corresponding solution estimates: this information is passed along to LGO for refinement within the given resource (function evaluation and/or runtime) limitations. Obviously, the quality of the solution obtained will essentially depend both on model instance difficulty and on the admissible computational effort. In spite of this general caveat, our results based on solving a selection of non-trivial global optimization test problems suggest that even a moderate amount of well-placed sampling effort enhanced by limited optimization can lead at least to reasonable or even to high quality results. Our numerical tests also indicate that LGO’s overall efficiency is often increased by using RSS as a presolver, both in resource-constrained and in completed LGO runs.Hungarian National Development Agency ; European Unio
Calibrating artificial neural networks by global optimization
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.Artificial neural networks (ANNs) are used extensively to model unknown or unspecified functional relationships between the input and output of a “black box” system. In order to apply the generic ANN concept to actual system model fitting problems, a key requirement is the training of the chosen (postulated) ANN structure. Such training serves to select the ANN parameters in order to minimize the discrepancy between modeled system output and the training set of observations. We consider the parameterization of ANNs as a potentially multi-modal optimization problem, and then introduce a corresponding global optimization (GO) framework. The practical viability of the GO based ANN training approach is illustrated by finding close numerical approximations of one-dimensional, yet visibly challenging functions. For this purpose, we have implemented a flexible ANN framework and an easily expandable set of test functions in the technical computing system Mathematica. The MathOptimizer Professional global–local optimization software has been used to solve the induced (multi-dimensional) ANN calibration problems.Özyeğin University ; Simulation and Optimization Project of Széchenyi István Universit
Benchmarking nonlinear optimization software in technical computing environments
No full textDue to copyright restrictions, the access to the full text of this article is only available via subscription.Our strategic objective is to develop a broadly categorized, expandable collection of test problems, to support the benchmarking of nonlinear optimization software packages in integrated technical computing environments (ITCEs). ITCEs—such as Maple, Mathematica, and MATLAB—support concise, modular and scalable model development: their built-in documentation and visualization features can be put to good use also in test model selection and analysis. ITCEs support the flexible inclusion of both new models and general-purpose solver engines for future studies. Within this broad context, in this article we review a collection of global optimization problems coded in Mathematica, and present illustrative and summarized numerical results obtained using the MathOptimizer Professional software package.DRDC-Atlantic ; the National Research Council of Canada ; the University of Ballarat, Australia; Özyegin University ; the TÁMOP, Széchenyi István Universit
Development and calibration of a currency trading strategy using global optimization
Get PDFWe have developed a new financial indicator—called the Interest Rate Differentials Adjusted for Volatility (IRDAV) measure—to assist investors in currency markets. On a monthly basis, we rank currency pairs according to this measure and then select a basket of pairs with the highest IRDAV values. Under positive market conditions, an IRDAV based investment strategy (buying a currency with high interest rate and simultaneously selling a currency with low interest rate, after adjusting for volatility of the currency pairs in question) can generate significant returns. However, when the markets turn for the worse and crisis situations evolve, investors exit such money-making strategies suddenly, and—as a result—significant losses can occur. In an effort to minimize these potential losses, we also propose an aggregated Risk Metric that estimates the total risk by looking at various financial indicators across different markets. These risk indicators are used to get timely signals of evolving crises and to flip the strategy from long to short in a timely fashion, to prevent losses and make further gains even during crisis periods. Since our proprietary model is implemented in Excel as a highly nonlinear “black box” computational procedure, we use suitable global optimization methodology and software—the Lipschitz Global Optimizer solver suite linked to Excel—to maximize the performance of the currency basket, based on our selection of key decision variables. After the introduction of the new currency trading model and its implementation, we present numerical results based on actual market data. Our results clearly show the advantages of using global optimization based parameter settings, compared to the typically used “expert estimates” of the key model parameters.post-prin
Optimized calibration of currency market strategies
Get PDFWe propose a new financial indicator and risk metric embedded in a currency trading model to assist investors in currency markets. Since our model is highly nonlinear, we utilize global optimization
technology to maximize the performance of the currency basket, based on our selection of key decision variables. We introduce the model and present numerical results based on actual market data
MathOptimizer: a nonlinear optimization package for mathematica users
Get PDFÖzyeğin University Technical ReportMathematica is an advanced software system that enables symbolic computing, numerics, program code development, model visualization and professional documentation in a unified framework. Our MathOptimizer software package serves to solve global and local optimization models developed using Mathematica. We introduce MathOptimizer’s key features and discuss its usage options that support a range of operational modes. The numerical capabilities of the package are illustrated by simple and more advanced examples, pointing towards a broad range of potential applications
Optimized design of dynamic networks with heuristic algorithms
No full textThis research investigates the design of reliable wireless communication networks in which all communication is carried over the wireless medium, every node exhibits mobility and is able to establish only a limited number of point-to-point links with other nodes in its neighbourhood. The design process aims to select a feasible subset of the potential links in such a way that both network reliability and network performance are maximised. We describe a graph-theoretic model for the type of network under consideration, define some graph-theoretic measures of network reliability in terms of this model, present methods for the optimisation of these reliability measures, and indicate their performance under simulation
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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