Abstract: The modeling of a utility function’s forms is a very interesting part of modern decision making theory. We apply a basic concept of the personal utility theory on determination of minimal net and maximal gross annual premium in general insurance. We introduce specific values of gross annual premium on the basis of a personal utility function, which is determined empirically by a short personal interview. Moreover, we introduce a new approach to the creation of a personal utility function by a fictive game and an aggregation of specific values by mixture operators.
Keywords: Utility function; Expected utility; Mixture operator; General Insurance
Abstract: In this paper we propose a Multilayer Perceptron Neural Network (MLP NN) consisting of fuzzy flip-flop neurons based on various fuzzy operations applied in order to approximate a real-life application, two input trigonometric functions, and two and six dimensional benchmark problems. The Bacterial Memetic Algorithm with Modified Operator Execution Order algorithm (BMAM) is proposed for Fuzzy Neural Networks (FNN) training. The simulation results showed that various FNN types delivered very good function approximation results.
Keywords: fuzzy flip-flop neurons; Fuzzy Neural Networks; Bacterial Memetic Algorithm with Modified Operator Execution Order
Abstract: We will be focused on the interpolation approach to a computation with fuzzy data. A definition of interpolation of fuzzy data, which stems from the classical approach, is proposed. We investigate another approach to fuzzy interpolation (published in ) with relaxed interpolation condition. We prove that even if the interpolation condition is relaxed the related algorithm gives an interpolating fuzzy function which fulfils the interpolation condition in the classical sense.
Keywords: Fuzzy function; fuzzy equivalence, fuzzy space, fuzzy interpolation, fuzzy rule base interpolation
Abstract: In this paper we work with nonparametric methods in modeling and analyzing the financial times series. We use the concept of fractal dimension for measuring the complexity of time series of observed financial data. The aim of this paper is to distinguish between the randomness and determinism of the financial information. We will compare the fractal analysis of the selected forward exchange rates. Fractal analysis has been introduced into financial time series by Mandelbrot and Peters. Due to the financial crisis this theory has gained new momentum. Fractal analysis indicates that conventional econometric methods are inadequate for analyzing financial time series. Adequate analysis of the financial time series allows us to predict precisely the future values and risks connected with portfolios that are influenced. We test for fractional dynamic behavior in a 1-month forward exchange rate USD into GBP and Gold Price against USD.
Keywords: fractal analysis; estimation dimension; long memory; financial time series
Abstract: This paper presents an adaptive-network-based fuzzy inference system (ANFIS) for long-term natural Electric consumption prediction. Six models are proposed to forecast annual Electric demand. 104 ANFIS have been constructed and tested in order to find the best ANFIS for Electric consumption. Two parameters have been considered in the construction and examination of plausible ANFIS models. The type of membership function and the number of linguistic variables are two mentioned parameters. Six different membership functions are considered in building ANFIS, as follows: the built-in membership function composed of the difference between two sigmoidal membership functions (dsig), the Gaussian combination membership function (gauss2), the Gaussian curve built-in membership function (gauss), the generalized bell-shaped built-in membership function (gbell), the Π-shaped built-in membership function (pi), psig. Also, a number for linguistic variables has been considered between 2 and 20. The proposed models consist of input variables such as: Gross Domestic Product (GDP) and Population (POP). Six distinct models based on different inputs are defined. All of the trained ANFIS are then compared with respect to the mean absolute percentage error (MAPE). To meet the best performance of the intelligent based approaches, data are pre-processed (scaled) and finally our outputs are post-processed (returned to its original scale). The ANFIS model is capable of dealing with both complexity and uncertainty in the data set. To show the applicability and superiority of the ANFIS, the actual Electric consumption in industrialized nations including the Netherlands, Luxembourg, Ireland, and Italy from 1980 to 2007 are considered. With the aid of an autoregressive model, the GDP and population by 2015 is projected and then with yield value and best ANFIS model, Electric consumption by 2015 is predicted.
Keywords: Natural Electric Demand; Long-Term prediction; Adaptive Network-based Fuzzy Inference System (ANFIS)
Abstract: In this paper a short general review of the main characteristics of risk management applications is given, where a hierarchical, multilevel risk management method can be applied in a fuzzy decision making environment. The given case study is a travel risk-level calculation based on the presented model. In the last section an extended model and a preliminary mathematical description is presented, where the pairwise comparison matrix of the grouped risk factors expands the previous principles.
Keywords: risk management; fuzzy multilevel decision making; comparison matrix
Abstract: Proposed in this study is a hybrid model for supporting the department selection process within Iran Amirkabir University. This research is a two-stage model designed to fully rank the organizational departments where each department has multiple inputs and outputs. First, the department evaluation problem is formulated by Data Envelopment Analysis (DEA) and separately formulates each pair of units. In the second stage, the pairwise evaluation matrix generated in the first stage is utilized to fully rank-scale the units via the Fuzzy Analytical Network Process (FANP). The FANP method adopted here uses triangular fuzzy numbers. ANP equipped with fuzzy logic helps in overcoming the impreciseness in the preferences. DEA-FANP ranking does not replace the DEA classification model; rather, it furthers the analysis by providing full ranking in the DEA context for all departments, efficient and inefficient.
Keywords: Data Envelopment Analysis (DEA); Fuzzy Analytical Network Process (FANP); Performance; Efficiency; Fully Rank
Abstract: A combining adaptive fuzzy-wavelet control algorithm is proposed for a class of continuous time unknown nonlinear systems. An application of wavelet networks to control problems of nonlinear systems is investigated in this work. A wavelet network is constructed as an alternative to a neural network to approximate a nonlinear system. Based on this wavelet network and fuzzy approximation, suitable adaptive control laws and appropriate parameter update algorithms for nonlinear uncertain (or unknown) systems are developed to achieve tracking performance. The stability analysis for the proposed control algorithm is provided. A nonlinear system simulation example is presented to verify the effectiveness of the proposed method.
Keywords: fuzzy control; adaptive control; wavelet approximation; feedback linearization
Abstract: Technological progress, responsible for the declining costs of computers, coupled with the advancement of computer adaptive software have promoted computer adaptive testing (CAT) in higher education, offering alternatives to the conventional paper and pencil examinations. The CAT testing process, statistically conducted through Item Response Theory, is able to react to the individual examinee, keeping examinees on target with test items of an appropriate level of difficulty. The basic goal of adaptive computer tests is to ensure the examinee is supplied questions that are challenging enough for them but not too difficult, which would lead to frustration and confusion. The paper presents a CAT system realized in MATLAB along with its development steps. The application can run from a Matlab command window, or it is possible to make a stand-alone application that does not require the installation of Matlab. The questions are written in a .txt file. This allows the examiner to easily modify and extend the question database, without specific knowledge of the syntax of any programming language. The only requirement is for the examiner (but it is only required) to follow a pre-determined format of question writing. The program enables the testing of student knowledge in C++.
Keywords: computer adaptive testing; Item Response Theory; e-assessment
Abstract: This paper presents the intelligent wheeled mobile robot motion control in unstructured environments. The fuzzy control of a wheeled mobile robot motion in unstructured environments with obstacles and slopes is proposed. Outputs of the fuzzy controller are the angular speed difference between the left and right wheels of the mobile robot and the mobile robot velocity. The simulation results show the effectiveness and the validity of the obstacle avoidance behavior in an unstructured environment and the velocity control of a wheeled mobile robot motion of the proposed fuzzy control strategy. Wireless sensor-based remote control of mobile robots motion in unstructured environments using the Sun SPOT technology is proposed. The proposed method has been implemented on the miniature mobile robot Khepera that is equipped with sensors. Finally, the effectiveness and efficiency of the proposed sensor-based remote control strategy are demonstrated by experimental studies and good experimental results.
Keywords: intelligent wheeled mobile robot; motion control; unknown and unstructured environments; obstacles and slopes; fuzzy control strategy; wireless sensor-based remote control; Sun SPOT technology; simulation results; experimental studies; mobile robot Khepera
Abstract: These days, data mining is frequently used as a technology for analysing the huge amounts of data collected in sport. Basketball is one of most popular sports. Due to its dynamics, a large number of events happen during a single game. Basketball statisticians have the task of noting as many of these events as possible, in order to provide for their analysis. These data are collected by special software applications. In this paper, we used data from the First B basketball league for men in Serbia, for seasons 2005/06, 2006/07, 2007/08, 2008/09 and 2009/2010. During these five seasons, a total of 890 games were played. These data were analyzed using the feedforward technique in neural networks, which is the most often used technique in analyzing nonlinear sports data. As a final result, we concluded that the most important elements in basketball are two-point shots under the hoop and the defensive rebound, i.e. game "in the paint".
Keywords: neural networks; data mining; basketball