Current Issue : July - September Volume : 2012 Issue Number : 3 Articles : 5 Articles
This paper demonstrates an attempt to incorporate a simple and generic constraint handling technique to the Probability\r\nCollectives (PC) approach for solving constrained optimization problems. The approach of PC optimizes any complex system\r\nby decomposing it into smaller subsystems and further treats them in a distributed and decentralized way. These subsystems can\r\nbe viewed as a Multi-Agent System with rational and self-interested agents optimizing their local goals. However, as there is no\r\ninherent constraint handling capability in the PC approach, a real challenge is to take into account constraints and at the same\r\ntime make the agents work collectively avoiding the tragedy of commons to optimize the global/system objective. At the core\r\nof the PC optimization methodology are the concepts of Deterministic Annealing in Statistical Physics, Game Theory and Nash\r\nEquilibrium. Moreover, a rule-based procedure is incorporated to handle solutions based on the number of constraints violated\r\nand drive the convergence towards feasibility. Two specially developed cases of the Circle Packing Problem with known solutions\r\nare solved and the true optimum results are obtained at reasonable computational costs. The proposed algorithm is shown to be\r\nsufficiently robust, and strengths and weaknesses of the methodology are also discussed....
The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy\r\nmodels in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization\r\ndeserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in\r\nthe context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection\r\n(namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to\r\nbecome efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use\r\nof advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization\r\nvehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the\r\nsearch space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism\r\nof forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets\r\nis presented....
This paper deals with transactions with their classes. The classes represent the difference of conditions in the data collection. This\r\npaper redefines two kinds of supports: characteristic support and possible support. The former one is based on specific classes\r\nassigned to specific patterns. The latter one is based on the minimum class in the classes. This paper proposes a new method\r\nthat efficiently discovers patterns whose characteristic supports are larger than or equal to the predefined minimum support by\r\nusing their possible supports. Also, this paper verifies the effect of the method through numerical experiments based on the data\r\nregistered in the UCI machine learning repository and the RFID (radio frequency identification) data collected from two apparel\r\nshops....
We examine the efficiency of four machine learning algorithms for the fusion of several biometrics modalities to create a\r\nmultimodal biometrics security system. The algorithms examined are Gaussian Mixture Models (GMMs), Artificial Neural\r\nNetworks (ANNs), Fuzzy Expert Systems (FESs), and Support VectorMachines (SVMs). The fusion of biometrics leads to security\r\nsystems that exhibit higher recognition rates and lower false alarms compared to unimodal biometric security systems. Supervised\r\nlearning was carried out using a number of patterns from a well-known benchmark biometrics database, and the validation/testing\r\ntook place with patterns from the same database which were not included in the training dataset. The comparison of the algorithms\r\nreveals that the biometrics fusion system is superior to the original unimodal systems and also other fusion schemes found in the\r\nliterature....
The track of developing Economic Order Quantity (EOQ) models with uncertainties described as fuzzy numbers has been very\r\nlucrative. In this paper, a fuzzy Economic Production Quantity (EPQ) model is developed to address a specific problem in a\r\ntheoretical setting. Not only is the production time finite, but also backorders are allowed. The uncertainties, in the industrial\r\ncontext, come from the fact that the production availability is uncertain as well as the demand. These uncertainties will be handled\r\nwith fuzzy numbers and the analytical solution to the optimization problem will be obtained. A theoretical example from the\r\nprocess industry is also given to illustrate the new model....
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