Generalizing the Min-Max Regret Criterion using Ordered Weighted Averaging

In decision making under uncertainty, several criteria have been studied to aggregate the performance of a solution over multiple possible scenarios, including the ordered weighted averaging (OWA) criterion and min-max regret. This paper introduces a novel generalization of min-max regret, leveraging the modeling power of OWA to enable a more nuanced expression of preferences in handling regret values. This new OWA regret approach is studied both theoretically and numerically. We derive several properties, including polynomially solvable and hard cases, and introduce an approximation algorithm. Through computational experiments using artificial and real-world data, we demonstrate the advantages of our OWAR method over the conventional min-max regret approach, alongside the effectiveness of the proposed clustering heuristics

DEVELOPMENT OF A METHOD FOR DETERMINING THE OPTIMAL CONTROL TRAJECTORY FOR THE PERIODIC PROCESSES

The development of new converting technologies and increasing the level of automation leads to an increase in the processing speed of available resources. This means that in case of inefficient or inefficient use of resources, the potential of economic advantage is lost, and the negative consequences of such losses can be identified too late. In such conditions, the requirements for the selection of the criterion of optimization and the validity of the principles of optimal control increase. Moreover, the approach used may vary depending on the daily change in prices for energy products and fluctuations in the level of demand for manufactured products. Despite the fact that such an approach seems obvious, today energy-intensive production suffers significant losses associated with the use of an inadequate model of technological operation, a subjective approach to the choice of optimization criterion and optimization method. The task of developing a method for determining the optimal trajectory of managing energy-intensive industries with a changing level of demand for final products is posed. The solution to this problem consists of several stages. At the first stage, a model of a technological operation is being created, within the framework of which all significant factors affecting the decision result are taken into account. At the second stage, a global operation model is created, the input and output products of which are converted to comparable value values. At the third stage, the optimization criterion for the continuous operation of the system and the mode of incomplete use of its production capabilities is selected. At the final stage, the control path for intermittent and continuous modes of operation is determined. Thus, the aim of research is development of a method for determining the optimal control trajectory in systems of energy-intensive systems, depending on the level of demand for the final product

Journal of Telecommunications and Information Technology, 2008, nr 4

kwartalni

Journal of Telecommunications and Information Technology, 2009, nr 3

kwartalni

An Invistigation About The Fuzzy Logic Aplications In Construction Management

In this study some fuzzy logic aplications in Construction Management (CM) discipline has been investigated. By this way tried to have an idea about the usifulness and practicability of fuzzy logic in the area of CM. For the aim of investigation of the fuzzy logic aplications’ provides in CM discipline; there have been made a literature invistigation on different sub-branches of CM. Because of CM is a very large area, this invistigations’ scope is determined on the some top quality journals’ papers in the Science Situation Index (SCI). Chosen researchs’ results from this investigations given in the text. It’s confirmed that the applications in this area before, scattered in four main categories, including: decision making, performance, evaluation/assessment; and modeling. Olso optimisation is another aim for using fuzzy logic

Curvature-based sparse rule base generation for fuzzy rule interpolation

Fuzzy logic has been successfully widely utilised in many real-world applications. The most common application of fuzzy logic is the rule-based fuzzy inference system, which is composed of mainly two parts including an inference engine and a fuzzy rule base. Conventional fuzzy inference systems always require a rule base that fully covers the entire problem domain (i.e., a dense rule base). Fuzzy rule interpolation (FRI) makes inference possible with sparse rule bases which may not cover some parts of the problem domain (i.e., a sparse rule base). In addition to extending the applicability of fuzzy inference systems, fuzzy interpolation can also be used to reduce system complexity for over-complex fuzzy inference systems. There are typically two methods to generate fuzzy rule bases, i.e., the knowledge driven and data-driven approaches. Almost all of these approaches only target dense rule bases for conventional fuzzy inference systems. The knowledge-driven methods may be negatively affected by the limited availability of expert knowledge and expert knowledge may be subjective, whilst redundancy often exists in fuzzy rule-based models that are acquired from numerical data. Note that various rule base reduction approaches have been proposed, but they are all based on certain similarity measures and are likely to cause performance deterioration along with the size reduction. This project, for the first time, innovatively applies curvature values to distinguish important features and instances in a dataset, to support the construction of a neat and concise sparse rule base for fuzzy rule interpolation. In addition to working in a three-dimensional problem space, the work also extends the natural three-dimensional curvature calculation to problems with high dimensions, which greatly broadens the applicability of the proposed approach. As a result, the proposed approach alleviates the ‘curse of dimensionality’ and helps to reduce the computational cost for fuzzy inference systems. The proposed approach has been validated and evaluated by three real-world applications. The experimental results demonstrate that the proposed approach is able to generate sparse rule bases with less rules but resulting in better performance, which confirms the power of the proposed system. In addition to fuzzy rule interpolation, the proposed curvature-based approach can also be readily used as a general feature selection tool to work with other machine learning approaches, such as classifiers

Exploratory Analysis of Human Sleep Data

In this thesis we develop data mining techniques to analyze sleep irregularities in humans. We investigate the effects of several demographic, behavioral and emotional factors on sleep progression and on patient\u27s susceptibility to sleep-related and other disorders. Mining is performed over subjective and objective data collected from patients visiting the UMass Medical Center and the Day Kimball Hospital for treatment. Subjective data are obtained from patient responses to questions posed in a sleep questionnaire. Objective data comprise observations and clinical measurements recorded by sleep technicians using a suite of instruments together called polysomnogram. We create suitable filters to capture significant events within sleep epochs. We propose and employ a Window-based Association Rule Mining Algorithm to discover associations among sleep progression, pathology, demographics and other factors. This algorithm is a modified and extended version of the Set-and-Sequences Association Rule Mining Algorithm developed at WPI to support the mining of association rules from complex data types. We analyze both the medical as well as the statistical significance of the associations discovered by our algorithm. We also develop predictive classification models using logistic regression and compare the results with those obtained through association rule mining