Fuzzy Logic Theory and Applications in Uncertainty Management of Linguistic Evaluations for Students

Fuzzy logic can deal with information arising from perception and cognition that is uncertain, imprecise, vague, partially true, or without sharp boundaries. Fuzzy logic can be used for assigning linguistic grades and for decision making and data mining with those linguistic grades by teachers, instructors, and professors. Many aspects of fuzzy logic including fuzzy sets, linguistic variables, fuzzy rules, fuzzy math, fuzzy database queries, computational theory of perceptions, and computing with words are useful in uncertainty management of linguistic evaluations for students. This chapter provides many examples of this after describing the theory of fuzzy logic.

Harmony Search Algorithm and Fuzzy Logic Theory: An Extensive Review from Theory to Applications

In recent years, many researchers have utilized metaheuristic optimization algorithms along with fuzzy logic theory in their studies for various purposes. The harmony search (HS) algorithm is one of the metaheuristic optimization algorithms that is widely employed in different studies along with fuzzy logic (FL) theory. FL theory is a mathematical approach to expressing uncertainty by applying the conceptualization of fuzziness in a system. This review paper presents an extensive review of published papers based on the combination of HS and FL systems. In this regard, the functional characteristics of models obtained from integration of FL and HS have been reported in various articles, and the performance of each study is investigated. The basic concept of the FL approach and its derived models are introduced to familiarize readers with the principal mechanisms of FL models. Moreover, appropriate descriptions of the primary classifications acquired from the coexistence of FL and HS methods for specific purposes are reviewed. The results show that the high efficiency of HS to improve the exploration of FL in achieving the optimal solution on the one hand, and the capability of fuzzy inference systems to provide more flexible and dynamic adaptation of the HS parameters based on human perception on the other hand, can be a powerful combination for solving optimization problems. This review paper is believed to be a useful resource for students, engineers, and professionals.

Improving the quality of automated VIS–grading of Scots pine seeds using fuzzy logic algorithm

The automation of Scots pine seeds grading in the visible wavelength region – VIS-grading – is promising for conducting breeding and genetic experiments. This will reduce time costs and increase the accuracy of seed color classification compared to organoleptic techniques. When controlling VIS-grading, it is necessary to accurately detect and process the optical signal reflected from a single seed. The signal is based on the wavelength and amplitude of the optical beam. Earlier, using a spectrometer for Scots pine seeds from a natural stand of the Pavlovsky district of the Voronezh region, Russia, the boundaries of three spectrometric groups were established. In the real VIS-grading process, it is necessary to take into account the probabilistic deviations of random values of wavelengths and amplitudes. Therefore, on the basis of the Mamdani fuzzy logic theory developed an analyzing algorithm for controlling the VIS-grading quality. The algorithm consists of a sequence of logical terms that clearly define the specified VIS-grading seeds spectrometric parameters by a combination of wavelength and amplitude. The efficiency of Scots pine seeds VIS-grading using the algorithm is 98.9%.

Incorporating Fuzzy Logic in Harrod’s Economic Growth Model

This paper suggests the possibility of incorporating the methodology of fuzzy logic theory into Harrod’s economic growth model, a classic model of economic dynamics for studying the growth of a developing economy based on the assumption that an economy with only savings and investment income is in equilibrium when savings are equal to investment. This model was the first precursor to exogenous growth models, which in turn gave rise to endogenous growth models. This article therefore represents a first step towards introducing fuzzy logic into economic growth models. The study concerned considers consumption and savings to depend on income by means of uncertain factors, and investment to depend on the variation of income through the accelerator factor, which we consider uncertain. These conditions are used to determine the equilibrium growth rate of income and investment, as well as the uncertain values for these variables in terms of fuzzy numbers. As a result, the new model is shown to expand the classical model by incorporating uncertainty into its variables.

Implementation of reduced induction machine fuzzy logic control based on dSPACE-1104 R&D controller board

In this paper, we present our contribution in Induction Machine control field. The control we designed is based on fuzzy logic theory, this choice is motivated by the fact that this technique of control is suitable for the control of systems characterized by its parameters uncertainties and variations. The proposed control is optimized by using a genetic algorithm for fuzzy logic controller (FLC) gains tuning and by a good choice of calculation techniques used in FLC. Three versions of IM fuzzy logic control were validated by simulation. After that in order to be able to experimentally implement this control on dSPACE-1104, we proposed an optimized and reduced structure of the control. The experimental results proof the effectiveness and the satisfied performance of the proposed IM fuzzy control.

Financial market volatility based on complex network and fuzzy logic theory

Volatility is an inherent attribute of the financial market, which is usually expressed as the degree of volatility of financial asset prices. The volatility of the financial market means that there is uncertainty or risk in the market. This paper mainly studies financial market fluctuations based on complex networks and fuzzy logic theory. This article first systematically organizes and summarizes the theoretical construction of complex networks and fuzzy logic. In terms of complex networks, the definition of complex networks, the theory of commonly used functions (classical models of complex networks) and the solving methods are sorted out. In the construction of fuzzy logic theory, starting with quantifiable financial market volatility indicators, the construction models of realized volatility and implied volatility are discussed, and complex network models of implied volatility and model-free models are discussed. The theoretical construction methods were compared and analyzed. Finally, it summarizes the theoretical construction methods of implied volatility index and points out the advantages of model-free implied volatility as a market volatility and risk measurement index, which contains more effective future risk information and is based on implied volatility. The empirical research on indexes and complex network models has laid a theoretical foundation. Experimental data shows that the bond market and the foreign exchange market have the largest fluctuations in the correlation coefficient, reaching 0.35; followed by the stock market and the bond market, which is about 0.17; the stock market and foreign exchange market with the smallest fluctuations are about 0.08. The experimental results show that the financial market volatility research data based on complex networks and fuzzy logic theory is more accurate.

Discharge estimation for trapezoidal open channels using fuzzy transformation method to a flow equation

The aim of this paper is to implement the fuzzy logic theory in order to estimate the discharge for open channels, which is a well-known physical problem affected by many factors. The problem can be solved by Manning equation but the parameters present uncertainties as to their true-real values. Especially, the Manning n roughness coefficient, which is an empirically derived coefficient, presents quite high variation for different substrates. With the help of fuzzy logic and utilizing a fuzzy transformation method, it is possible to include the uncertainties of the problem in the calculation process. In this case, it is feasible to estimate the discharge, giving more emphasis on different uncertainty rates of the Manning roughness coefficient, while the rest of the parameters remain with constant or zero uncertainty level. By taking different a-cut levels, it was shown that the methodology gives realistic and reliable results, presenting with great accuracy the variations of the water discharge for trapezoidal open channels. This way, a possible underestimation or overestimation of the actual physical condition is avoided, by helping the engineers and researchers to obtain a more comprehensive view of the real physical conditions, thus making better management plans.