56,672 research outputs found
Kinetics of Kaymak separation
Full text linkU radu su prikazani rezultati eksperimentalnih ispitivanja kinetike procesa izdvajanja kajmaka iz mlijeka, s ciljem da se dobiveni podaci iskoriste kao podloga za razvoj novog proizvodnog tehnološkog postupka. Izdvajanje kajmaka obavljeno je u instalacijama laboratorijskih razmjera. U toku procesa, mlijeko je stalno zagrijavano na povišene temperature, a iznad slobodne površine strujao je ugrijani zrak. Parametri procesa, čiji je utjecaj na prinos kajmaka proučavan, bili su: visina sloja mlijeka, temperature mlijeka i zraka i brzina strujanja zraka. Dobiveni eksperimentalni rezultati ukazuju da se pogodnim izborom radnih parametara procesa, mogu ostvariti takve brzine izdvajanja kajmaka koje su nekoliko puta veće u odnosu na brzine procesa ostvarene pri tradicionalnim, postupcima proizvodnje.Kinetics of kaymak separation was studied on a laboratory scale experimental equipment. Obtained results were used for developing a new kaymak production process. During the kaymak separation, the bulk of milk was heated using a hot water heat exchanger. Drying of the kaymak layer, separated on the milk surface, was performed by hot airflow. Kaymak yield was analysed as a function of the following parameters: depth of the initial milk layer, temperature gradients in the bulk of milk as well as in the air over the milk surface, and the rate of airflow. Obtained experimental results have shown that the rate of the kaymak separation can be significantly increased by selecting the appropriate working parameters
A conceptual model of investor behavior
No full textBehavioral finance is a subdiscipline of finance that uses insights from cogni tive and social psychology to enrich our knowledge of how investors make their financial decisions. Agent-based artificial financial markets are bottomup models of financial markets that start from the micro level of individual investor behavior and map it into the macro level of aggregate market phenomena. It has been recognized in the literature, yet not fully explored, that such agent-based models are very suitable tool to generate or test various behavioral hypotheses. To pursue this research idea, first we develop a con ceptual model of individual investor that consists of a cognitive model of the investor and a description of the investment environment. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as learning, adapting, and evolving entity that perceives the environment, pro cesses information, acts upon it, and updates its internal states. This con ceptual model can be used to build stylized representations of (classes of) individual investors, and further studied within the paradigm of agent-based artificial financial markets
Modeling loss aversion and biased self-attribution using fuzzy aggregation
No full textIn this paper we use an agent-based stock market to study how investor performance and market predictions influence investor sentiment and confidence. Investor sentiment is modeled using a generalized average operator, which has been proposed in the fuzzy literature as an index of optimism. Our simulations show the impact of loss aversion on investor optimism, and the emergence of investor overconfidence through biased self-attribution. Computational models of financial markets show potential for studying the dynamics of investor psychology with respect to various market feedbacks, while the fuzzy aggregation operator used provides a convenient way of modeling those psychological effect
Overconfident investors in the LLS agent-based artificial financial market
No full textAgent-based artificial financial markets are bottom-up models of financial markets which explore the mapping from the micro level of individual investor behavior into the macro level of aggregate market phenomena. It has been recently recognized in the literature that such (agentbased) models are potentially a very suitable tool to generate or test various behavioral hypotheses. One of the psychological biases that received a lot of attention in financial studies, both mainstream and behavioral, is the phenomena of investor overconfidence. This paper studies overconfident investors in the agent-based artificial financial market based on the Levy, Levy, Solomon (2000) model. Overconfidence is modeled as miscalibration, i.e. as underestimated risk of expected returns. We find that overconfident investors create less frequent but more extreme bubbles and crashes when compared to the unbiased efficient market believers of the original model. When investors are modeled to exhibit a biased self-attribution, they quickly move to the state of high overconfidence and remain there. With an unbiased self-attribution, on the other hand, investor overconfidence varies greatly, but around a moderate level of overconfidenc
Weighted Constraints in Fuzzy Optimization
Full text linkMany practical optimization problems are characterized by someflexibility in the problem constraints, where this flexibility canbe exploited for additional trade-off between improving theobjective function and satisfying the constraints. Especially indecision making, this type of flexibility could lead to workablesolutions, where the goals and the constraints specified bydifferent parties involved in the decision making are traded offagainst one another and satisfied to various degrees. Fuzzy setshave proven to be a suitable representation for modeling this typeof soft constraints. Conventionally, the fuzzy optimizationproblem in such a setting is defined as the simultaneoussatisfaction of the constraints and the goals. No additionaldistinction is assumed to exist amongst the constraints and thegoals. This report proposes an extension of this model forsatisfying the problem constraints and the goals, where preferencefor different constraints and goals can be specified by thedecision-maker. The difference in the preference for theconstraints is represented by a set of associated weight factors,which influence the nature of trade-off between improving theoptimization objectives and satisfying various constraints.Simultaneous weighted satisfaction of various criteria is modeledby using the recently proposed weighted extensions of(Archimedean) fuzzy t-norms. The weighted satisfaction of theproblem constraints and goals are demonstrated by using a simplefuzzy linear programming problem. The framework, however, is moregeneral, and it can also be applied to fuzzy mathematicalprogramming problems and multi-objective fuzzy optimization.wiskundige programmering;fuzzy sets;optimalisatie
Extended Fuzzy Clustering Algorithms
Full text linkFuzzy clustering is a widely applied method for obtaining fuzzy models from data. Ithas been applied successfully in various fields including finance and marketing. Despitethe successful applications, there are a number of issues that must be dealt with in practicalapplications of fuzzy clustering algorithms. This technical report proposes two extensionsto the objective function based fuzzy clustering for dealing with these issues. First, the(point) prototypes are extended to hypervolumes whose size is determined automaticallyfrom the data being clustered. These prototypes are shown to be less sensitive to a biasin the distribution of the data. Second, cluster merging by assessing the similarity amongthe clusters during optimization is introduced. Starting with an over-estimated number ofclusters in the data, similar clusters are merged during clustering in order to obtain a suitablepartitioning of the data. An adaptive threshold for merging is introduced. The proposedextensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resultingextended algorithms are given. The properties of the new algorithms are illustrated invarious examples.fuzzy clustering;cluster merging;similarity;volume prototypes
Feature selection using fuzzy objective functions
No full textOne of the most important stages in data preprocessing for data mining is feature selection. Real-world data analysis, data mining, classification and modeling problems usually involve a large number of candidate inputs or features. Less relevant or highly correlated features decrease, in general, the classification accuracy, and enlarge the complexity of the classifier. Feature selection is a multicriteria optimization problem, with contradictory objectives, which are difficult to properly describe by conventional cost functions. The use of fuzzy decision making may improve the performance of this type of systems, since it allows an easier and transparent description of the different criteria used in the feature selection process. In previous work an ant colony optimization algorithm for feature selection was presented, which minimizes two objectives: number of features and classification error. Two pheromone matrices and two different heuristics are used for each objective. In this paper, a fuzzy objective function is proposed to cope with the difficulty of weighting the different criteria involved in the optimization algorithm
A Lotting Method for Electronic Reverse Auctions
Full text linkAn increasing number of commercial companies are using online reverse auctions for their sourcing activities. In reverse auctions, multiple suppliers bid for a contract from a buyer for selling goods and/or services. Usually, the buyer has to procure multiple items, which are typically divided into lots for auctioning purposes. By steering the composition of the lots, a buyer can increase the attractiveness of its lots for thesuppliers, which can then make more competitive offers, leading to larger savings for the procuring party. In this paper, a clustering-based heuristic lotting method is proposed for reverse auctions. Agglomerative clustering is used for determining the items that will be put in the same lot. A suitable metric is defined, which allows the procurer to incorporate various approaches to lotting. The proposed lotting method has been tested for the procurement activities of a consumer packaged goods company. The results indicate that the proposed strategy leads to 2-3% savings, while the procurement experts confirm that the lots determined by the proposed method are acceptable given the procurement goals.e-commerce;reverse auctions;hierarchical clustering;lotting;e-procurement
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