Prediction of Sunspots using Fuzzy Logic: A Triangular Membership Function-based Fuzzy C-Means Approach

Azam, Muhammad Hamza; Hasan, Mohd Hilmi; Kadir, Said Jadid Abdul; Hassan, Saima

doi:10.14569/ijacsa.2021.0120245

Cited by 8 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In some cases or applications, FCM is more effective, robust, and consistent in performance as compared to other clustering algorithms [59]. Based on the data in Table 17, it is known that the cluster formation process stops at the 27th iteration, with a different value of 0.000000053240 or smaller than epsilon.…”

Section: 5determining Cluster Of Villagementioning

confidence: 99%

Determining rural development priorities using a hybrid clustering approach: a case study of South Sulawesi, Indonesia

2023

IJATEE

View full text Add to dashboard Cite

South Sulawesi province implements a village assistance program. The program aims to ensure the successful implementation of village development activities, reduce urbanization, and address development problems in rural areas. This research discusses finding alternative solutions to determining the priority status of villages that need assistance from experts. Village assistance is an activity in the context of developing self-sufficiency and feasibility of

show abstract

Section: 5determining Cluster Of Villagementioning

confidence: 99%

Determining rural development priorities using a hybrid clustering approach: a case study of South Sulawesi, Indonesia

2023

IJATEE

View full text Add to dashboard Cite

show abstract

“…(2) The cloud subset is evenly distributed. It is objective and reasonable that the membership degree of abscissa value of intersection X in is set as 0.5 because there can bring good execution performance which is commonly used in the related fuzzy control research in the references [43][44][45]. When the Ex and X in have already gained the En can be resolved by the following Formula (7):…”

Section: Fuzziness Of Observation Variables and Control Variablesmentioning

confidence: 99%

Intelligent control decision integrating fuzziness and randomness for automatic management of cash flow

Wang,

Fei,

Feng

2024

PLoS ONE

View full text Add to dashboard Cite

Automatic management of cash flow from the perspective of cybernetics decisions can improve work efficiency and accuracy of cash flow management. Disadvantage of traditional fuzzy control method is that it only expresses fuzziness and ignores randomness. The automatic management of cash flow involves variables representing the fuzziness and randomness of human cognition which need new calculation methods to solve. Based on fuzzy control this paper proposes a cloud set control decision method for cash flow management. Cloud set and its I operation and P operation are described. Methods are studied including observation variables and control variables, fuzziness of observation variables and control variables, description of rules, and cloud reasoning based on cloud set. The method is applied successfully in automatic management of cash flow in which control amount of expenditure intensity is -2.285. It is shown that this method can effectively obtain reasonable control quantities considering fuzzy and random properties by the comparison with fuzzy control method. The method for automatic management of cash flow proposed has greater objectivity and effectiveness for the integration of fuzzy and randomness representing human cognition and decision.

show abstract

“…Another study delved into the impact of different membership function types, specifically triangular, trapezoidal, and Gaussian, on the performance of a fuzzy logic controller [38]. In a different context, researchers employed two approaches to generate Gaussian and triangular fuzzy membership functions using fuzzy c-means for predicting sunspots [39]. These various investigations collectively www.ijacsa.thesai.org contribute to our understanding of the significance of membership functions and their impact on ANFIS model performance in different applications.…”

Section: Related Workmentioning

confidence: 99%

Influence of Membership Function and Degree on Sorghum Growth Prediction Models in Machine Learning

Rahman,

Budianta

et al. 2023

IJACSA

View full text Add to dashboard Cite

Rapid advances in science and technology have significantly changed plant growth modeling. The main contribution to this transformation lies in using Machine Learning (ML) techniques. This study focuses on sorghum, an important agricultural crop with significant economic implications. Crop yield studies include temperature, humidity, climate, rainfall, and soil nutrition. This research has a novelty: the input factors for predicting sorghum plant growth, namely the treatment of applying organic fertilizer and dolomite lime to sorghum planting land. The three predicted sorghum plant growth factors, namely Height, Biomass, and Panicle weight, are the reasons for using the Multiple Adaptive Neural Fuzzy Inference System (MANFIS) model. This research investigates the impact of Membership Function and Degree on the MANFIS model. A comprehensive comparison of various membership functions, including Gaussian, Triangular, Bell, and Trapezoidal functions, along with various degrees of membership, has been carried out. The dataset used includes data related to sorghum growth obtained from field experiments. The main objective was to assess the effectiveness of membership and degree functions in accurately predicting sorghum growth parameters, consisting of height, biomass, and panicle weight. This assessment uses metrics such as MAPE (Mean Absolute Percentage Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error) to evaluate the predictive performance of the MANFIS model when using four different types of membership functions and degrees. The results obtained the best level of accuracy in predicting panicle weight (ANFIS-3) with chicken manure treatment using the Trapezoidal membership function type and degree of membership function [3,3] with MAPE results of 5.77%, MAE of 0.2994, and RMSE of 0.395.

show abstract

Prediction of Sunspots using Fuzzy Logic: A Triangular Membership Function-based Fuzzy C-Means Approach

Cited by 8 publications

References 13 publications

Determining rural development priorities using a hybrid clustering approach: a case study of South Sulawesi, Indonesia

Determining rural development priorities using a hybrid clustering approach: a case study of South Sulawesi, Indonesia

Intelligent control decision integrating fuzziness and randomness for automatic management of cash flow

Influence of Membership Function and Degree on Sorghum Growth Prediction Models in Machine Learning

Contact Info

Product

Resources

About