Type-2 fuzzy sets and their variants
Ini John Umoeka; Veronica Neekay Akwukwuma
Abstract
The reliability of software product is seen as critical quality factor that cannot be overemphasized. Since real world application is loaded with high amount of uncertainty, such as applicable to software reliability, there should be a technique of dealing with such uncertainty. This paper presents a ...
Read More
The reliability of software product is seen as critical quality factor that cannot be overemphasized. Since real world application is loaded with high amount of uncertainty, such as applicable to software reliability, there should be a technique of dealing with such uncertainty. This paper presents a reliability model to effectively handle uncertainty in software data to enhance reliability prediction of software at the early (requirements and design) stages of Software Development Life Cycle (SDLC). In this paper, a hybrid methodology of Takagi Sugeno Kang (TSK)-based Interval Type-2 Fuzzy Logic System (IT2FLS) with Artificial Neural Network (ANN) learning is employed for the prediction of software reliability. The parameters of the model are optimized using Gradient Descent (GD) back-propagation method. Relevant reliability software requirement and design metrics and software size metrics are utilized as inputs. The proposed approach uses twenty-eight real software project data. The performance of the model is evaluated using five performance metrics and found to provide output values that are very close to the actual output showing better predictive accuracy.
Type-2 fuzzy sets and their variants
Mahmut Dirik
Abstract
In this study, a hybrid model for prediction issues based on IT2FLS and Particle Swarm Optimization (PSO) is proposed. The main contribution of this work is to discover the ideal strategy for creating an optimal value vector to optimize the membership function of the fuzzy controller. It should be emphasized ...
Read More
In this study, a hybrid model for prediction issues based on IT2FLS and Particle Swarm Optimization (PSO) is proposed. The main contribution of this work is to discover the ideal strategy for creating an optimal value vector to optimize the membership function of the fuzzy controller. It should be emphasized that the optimized fuzzy controller is a type-2 interval fuzzy controller, which is better than a type-1 fuzzy controller in handling uncertainty. The limiting membership functions of the type-2 fuzzy set domain is type-1 fuzzy sets, which explains the trace of uncertainty in this situation. The proposed optimization strategy was tested using ECG signal data. The accuracy of the proposed IT2FLS_PSO estimation technique was evaluated using a number of performance metrics (MSE, RMSE, error mean, error STD). RMSE and MSE with IT2FI were calculated as 0.1183 and 0.0535, respectively. With IT2FISPSO, these values were calculated as 0.0140 and 0.0029, respectively. The proposed PSO-optimized IT2FIS controller significantly improved its performance under various operating conditions. The simulation results show that PSO is effective in designing optimal type 2 fuzzy controllers. The experimental results show that the proposed optimization strategy significantly improves the prediction accuracy.
Type-2 fuzzy sets and their variants
Issam Kouatli
Abstract
Stock traders' forecasting strategies are mainly dependent on Technical Analysis (TA) indicators. However, some traders would follow their intuition and emotional aspects when trading instead of following the mathematically solid forecasting techniques of TA(s). The objective of this paper is to help ...
Read More
Stock traders' forecasting strategies are mainly dependent on Technical Analysis (TA) indicators. However, some traders would follow their intuition and emotional aspects when trading instead of following the mathematically solid forecasting techniques of TA(s). The objective of this paper is to help traders to rationalize their choices by generating the maximum and minimum tolerances of possible prices (termed in this paper as "fuzzy spectrum") and hence reducing their "emotional" trading decisions. This would be an important aspect towards avoiding an undesired outcome. Fuzzy logic has been used in this paper to identify such tolerances based on the most popular TA(s). Fuzzification of these TA(s) was used via a modular approach of fuzzy logic and by adopting "fuzzimetric sets" described in this paper to achieve the "fuzzy spectrum" of forecasted price tolerances when buying and selling decisions. Experimental results show the success of developing the "fuzzy spectrum" based on the "fuzzy" tolerances discovered from the TA(s) outputs. As a result, this paper contributes towards a better "rationalized" decision making when it comes to buying and selling stocks in this kind of industry.
Type-2 fuzzy sets and their variants
Uduak Umoh; Samuel Udoh; Abdultaofeek Abayomi; Alimot Abdulzeez
Abstract
Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) have shown popularity, superiority, and more accuracy in performance in a number of applications in the last decade. This is due to its ability to cope with uncertainty and precisions adequately when compared with its type-1 counterpart. In this paper, an ...
Read More
Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) have shown popularity, superiority, and more accuracy in performance in a number of applications in the last decade. This is due to its ability to cope with uncertainty and precisions adequately when compared with its type-1 counterpart. In this paper, an Interval Type-2 Fuzzy Logic System (IT2FLS) is employed for remote vital signs monitoring and predicting of shock level in cardiac patients. Also, the conventional, Type-1 Fuzzy Logic System (T1FLS) is applied to the prediction problems for comparison purpose. The cardiac patients’ health datasets were used to perform empirical comparison on the developed system. The result of study indicated that IT2FLS could coped with more information and handled more uncertainties in health data than T1FLS. The statistical evaluation using performance metrices indicated a minimal error with IT2FLS compared to its counterpart, T1FLS. It was generally observed that the shock level prediction experiment for cardiac patients showed the superiority of IT2FLS paradigm over T1FLS.
Type-2 fuzzy sets and their variants
Uduak Umoh; Imo Eyoh; Etebong Isong; Anietie Ekong; Salvation Peter
Abstract
Several attempts had been made to analyze emotion words in the fields of linguistics, psychology and sociology; with the advent of computers, the analyses of these words have taken a different dimension. Unfortunately, limited attempts have so far been made to using interval type-2 fuzzy logic (IT2FL) ...
Read More
Several attempts had been made to analyze emotion words in the fields of linguistics, psychology and sociology; with the advent of computers, the analyses of these words have taken a different dimension. Unfortunately, limited attempts have so far been made to using interval type-2 fuzzy logic (IT2FL) to analyze these words in native languages. This study used IT2FL to analyze Igbo emotion words. IT2F sets are computed using the interval approach method which is divided into two parts: the data part and the fuzzy set part. The data part preprocessed data and its statistics computed for the interval that survived the preprocessing stages while the fuzzy set part determined the nature of the footprint of uncertainty; the IT2F set mathematical models for each emotion characteristics of each emotion word is also computed. The data used in this work was collected from fifteen subjects who were asked to enter an interval for each of the emotion characteristics: Valence, Activation and Dominance on an interval survey of the thirty Igbo emotion words. With this, the words are being analyzed and can be used for the purposes of translation between vocabularies in consideration to context.
