scholarly journals Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zuzana Janková ◽  
Petr Dostál
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Stock Market ◽  
Fuzzy Sets ◽  
Financial Time Series ◽  
Fuzzy Model ◽  
Exchange Traded Funds ◽  
International Equity Markets

Extensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzy sets and compared with the type-1 fuzzy model. The case study aims to create a model to support investment decisions in Exchange-Traded Funds (ETFs) listed on international equity markets. The created models of type-2 fuzzy logic are compared with the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. The results reflect the capability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic models decreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine the different levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.

scholarly journals Investment Decision Support Based on Interval Type-2 Fuzzy Expert System

2021 ◽  
Vol 32 (2) ◽  
pp. 118-129
Author(s):  
Zuzana Janková ◽  
Dipak Kumar Jana ◽  
Petr Dostál
Keyword(s):  
Fuzzy Logic ◽  
Financial Markets ◽  
Degrees Of Freedom ◽  
Chaotic Behavior ◽  
Investment Decision ◽  
Fuzzy Model ◽  
Financial Model ◽  
Interval Type

The decision-making process on investing in financial markets is a very complex and difficult task, mainly due to the chaotic behavior and high uncertainty in the development of the prices of investment instruments. For this reason, financial markets are increasingly using means of artificial intelligence, namely fuzzy logic, which is able to capture the nonlinear behavior.Fuzzy logic provides a way to draw definitive conclusions from vague, ambiguous, or inaccurate information.However, there are some drawbacks associated with type-1 fuzzy logic, so the type-2 fuzzy logic comes forward, which can work with greater uncertainty. Type-2 fuzzy logic works with a new third dimension fuzzy set that provides additional degrees of freedom and allows to model and process numerical and linguistic uncertainties directly. The paper applies type-2 fuzzy logic to the stock market with the aim to create a simple and understandable model for deciding on investing in investment instruments, which is important for investors in this area. The proposed type-2 fuzzy model uses return, risk, dividend and total expense ratio of ETF as input variables. The created system is able to generate aggregated models from a certain number of language rules, which allows the investor to understand the created financial model. Using type-2 fuzzy logic can lead to more realistic and accurate results than type-1 fuzzy logic.

scholarly journals Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

2011 ◽  
Vol 6 (3) ◽  
pp. 503 ◽  
Author(s):  
Nazanin Sahab ◽  
Hani Hagras
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Real World ◽  
Superior Performance ◽  
Great Success ◽  
Real World Applications ◽  
Interval Type ◽  
Linguistic Labels

Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.

scholarly journals Type-2 Fuzzy Logic in Pair Formation

2018 ◽  
Vol 10 (1) ◽  
pp. 94
Author(s):  
Lakshmi Shrinivasan ◽  
J. l R. Rao
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Ground Truth ◽  
Fuzzy Logic System ◽  
Pair Formation ◽  
Type Reduction ◽  
And Performance ◽  
Logic System

<span>This paper gives an overview of Type-2 Fuzzy sets (T2FSs) and Type-2 fuzzy Logic system (T2FLS) considering one aviation scenario. The existing type-1 Fuzzy system has limited capability to handle the uncertainty directly. In order to overcome the limitations of Type-1 fuzzy Logic system (T1FLS), a next level of fuzzy set is introduced, that is known as T2FSs. Here we will discuss about: Type-2 fuzzy sets, type-2 membership functions, inference engine, type reduction and defuzzification. Pair formation is the undertaken aviation scenario which is very critical in a fighting situation. Crisp data are taken by the sensors of aircraft and with the techniques of data fusion, a constant decision is passed whether two aircrafts can achieve pair formation or not. Experiments are evaluated and performance is compared with ground truth and existing T1FLS, which proves better in terms of decision making while a certain amount of uncertainty is present</span>.

Realizing Interval Type-2 Fuzzy Systems with Type-1 Fuzzy Systems

2012 ◽  
pp. 412-427
Author(s):  
Mamta Khosla ◽  
R K Sarin ◽  
Moin Uddin ◽  
Satvir Singh ◽  
Arun Khosla
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Mean Square Error ◽  
Fuzzy Systems ◽  
Mean Square ◽  
Fuzzy Logic Systems ◽  
Interval Type

In this chapter, the authors have realized Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) with the average of two Type-1 Fuzzy Logic Systems (T1 FLSs). The authors have presented two case studies by applying this realization methodology on (i) an arbitrary system, where an IT2 FLS is considered, in which its footprint of uncertainty (FOU) is expressed using Principal T1 FS+FOU approach, and the second (ii) the Mackey-Glass time-series forecasting. In the second case study, T1 FLS is evolved using Particle Swarm Optimization (PSO) algorithm for the Mackey-Glass time-series data with added noise, and is then upgraded to IT2 FLS by adding FOU. Further, four experiments are conducted in this case study for four different noise levels. For each case study, a comparative study of the results of the average of two T1 FLSs and the corresponding IT2 FLS, obtained through computer simulations in MATLAB environment, is presented to demonstrate the effectiveness of the realization approach. Very low values of Mean Square Error (MSE) and Root Mean Square Error (RMSE) demonstrate that IT2 FLS performance is equivalent to the average of two T1 FLSs. This approach is helpful in the absence of the availability of development tools for T2 FLSs or because of complexity and difficulty in understanding T2 FLSs that makes the implementation difficult. It provides an easy route to the simulation/realization of IT2 FLSs and by following this approach, all existing tools/methodologies for the design, simulation and realization of T1 FLSs can be directly extended to T2 FLSs.

Overview of Type-2 Fuzzy Logic Systems

2012 ◽  
Vol 2 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Ahmad Taher Azar
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Systems ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Systems ◽  
Logic Systems

Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 fuzzy set (T1FS) does not capture uncertainty in its manifestations when it arises from vagueness in the shape of the membership function. Such uncertainties need to be depicted by fuzzy sets that have blur boundaries. The imprecise boundaries of a type-2 fuzzy set (T2FS) give rise to truth/membership values that are fuzzy sets in [0], [1], instead of a crisp number. Type-2 fuzzy logic systems (T2FLSs) offer opportunity to model levels of uncertainty which traditional fuzzy logic type1 struggles. This extra dimension gives more degrees of freedom for better representation of uncertainty compared to type-1 fuzzy sets. A type-1 fuzzy logic system (T1FLSs) inference produces a T1FS and the result of defuzzification of the T1FS, a crisp number, whereas a T2FLS inference produces a type-2 fuzzy set, its type-reduced fuzzy set which is a T1FS and the defuzzification of the type-1 fuzzy set. The type-reduced fuzzy set output gives decision-making flexibilities. Thus, FLSs using T2FS provide the capability of handling a higher level of uncertainty and provide a number of missing components that have held back successful deployment of fuzzy systems in decision making.

scholarly journals Performance Analysis of PI, T1NFC, and T2NFC of Indirect Vector Control-Based Induction Motor Using DSpace-2812

2021 ◽  
Vol 54 (5) ◽  
pp. 671-682
Author(s):  
Repana Ramanjan Prasad ◽  
Gadwala Durgasukuamar
Keyword(s):  
Fuzzy Logic ◽  
Vector Control ◽  
Fuzzy Sets ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Performance Characteristics ◽  
Load Torque ◽  
Neuro Fuzzy

A type 2 Neuro-Fuzzy torque controller for indirect vector control (IVC) based induction motor (IM) driving is presented in this work. In various operating modes, a linear fixed-gain proportional-integral (PI) based speed controller is employed in indirect vector control of an IM drive (IMD). To achieve high performance, the PI controller (PIC) requires precise and accurate gain parameters. The PIC gain values have been tuned for a specific operating point and may not perform satisfactorily when the load torque and operating point change. To enhance dynamic performance over a wide speed range and reduce load torque ripple, the PIC is replaced by a Type-1 neuro-fuzzy logic controller (T1NFC). The T1FLC is simple, easy to use, and successful at dealing with nonlinear control systems without the use of complex mathematical equations. Instead, it relies on simple logical rules that are decided by an expert. The T1NFC is replaced by a Type-2 neuro-fuzzy logic controller to enhance controller performance. Owing to the availability of three-dimensional control with type-reduction technique (i.e. Type-2 fuzzy sets and Type-2 reducer set) in the defuzzification process, the T2NFC effectively handles the large footprint of uncertainties compared to the T1NFC, whereas the T1NFC have only Type-1 fuzzy sets and a single membership function. The T2NFC using MATLAB Simulink is used to observe the induction motor performance characteristics like, stator phasor current, torque, and speed under various operating situations. T2NFC controllers provide better driving performance characteristics than PI and T1NFC controllers. When associated to the PI and T1NFC controllers, the suggested T2NFC greatly reduce the amount of ripple in the torque and stator current of the IM drive. Practical validation is also performed with a 3.7 KW IM drive and a DSP 2812 controller for real-time examination of the drive parameters.

scholarly journals Modeling the Context of the Problem Domain of Time Series with Type-2 Fuzzy Sets

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2947
Author(s):  
Anton A. Romanov ◽  
Aleksey A. Filippov ◽  
Valeria V. Voronina ◽  
Gleb Guskov ◽  
Nadezhda G. Yarushkina
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Uncertainty Modeling ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Time Series Modeling ◽  
Problem Domain ◽  
Contextual Modeling

Data analysis in the context of the features of the problem domain and the dynamics of processes are significant in various industries. Uncertainty modeling based on fuzzy logic allows building approximators for solving a large class of problems. In some cases, type-2 fuzzy sets in the model are used. The article describes constructing fuzzy time series models of the analyzed processes within the context of the problem domain. An algorithm for fuzzy modeling of the time series was developed. A new time series forecasting scheme is proposed. An illustrative example of the time series modeling is presented. The benefits of contextual modeling are demonstrated.

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

An interval type-2 hesitant fuzzy best-worst method

2021 ◽  
pp. 1-28
Author(s):  
Ashraf Norouzi ◽  
Hossein Razavi hajiagha
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Fuzzy Ahp ◽  
Uncertain Data ◽  
Interval Type ◽  
Best Worst Method

Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension.

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