Earticle

초록

영어

Trend Impact Analysis is a prominent hybrid method has been used in future studies with a modified surprise-free forecast. It considers experts' perceptions about how future events may change the surprise-free forecast. It is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using adaptive neuro-fuzzy inference system (ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes.

목차

Abstract
1. Introduction
2. TIA Framework and Related Models
3. TIA Algorithm by ANFIS Approach
4. Simulation
5. Conclusions
References

키워드

저자

• Yong-Gil Kim [ Professor, Department of Computer Security, Chosun College of Science & Technology, Korea ]
• Kang-Yeon Lee [ Professor, Department of Electricity, Chosun College of Science & Technology, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380