Towards a new approach of fuzzy cognitive maps

Mpelogianni, Vassiliki; Groumpos, Peter P.

doi:10.1109/iisa.2016.7785376

Cited by 8 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At each time point, the value of each idea A i is determined using a block function f from the sum of all the other concepts' influences and the total effect's limitation using the following rule [ 16 ]:

where A i t +1 and A i t are the concept's values C i at time t + 1 and t , respectively, A j t is the significance of the notion C j at time t , W ji is the connection's weight in the direction C j to the meaning C i , and f is a block function that is used to limit the concept's value to a certain range, commonly in the interval [0, 1] [ 17 , 18 ].…”

Section: Fcm Methodologymentioning

confidence: 99%

“…The simulation activation function used calculates the value Α j of a C j concept at the end of an iteration as the sum of its causal concepts' contributions at the start of the iteration [ 6 , 14 , 18 ]:

where A j ( t ) is the value of the concept C j at the end of the iteration, A i ( t − 1) is the value of the notation C j at the beginning of the iteration, w ij is the weight of the relation between C i and C j , and f is a threshold function, which is used to normalize the values in each step. The process assumes that the weight table includes an autocorrelation by placing a unit value in the main diagonal of the table (the new value of the concept necessarily equals the previous value plus (or minus) the contribution of the other concepts associated with it).…”

Section: Fcm Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Xiao

Chen

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Individual and team performance can be improved by utilizing “smart” devices and applications that are connected through networks. In sports, the Internet of Things (IoT) refers to all of the “smart” devices and applications linked through networks to reduce injuries to the bare minimum, develop advanced training techniques, and apply analytical advanced sports improvement methodologies to improve sports performance in general. The Internet of Things (IoT) in sports is closely related to the objective of both security and privacy in sports, which has become a topic of crucial concern for the sports business in recent years, as evidenced by the adoption of IoT in sports years. For this reason, security flaws can have catastrophic consequences, including the disclosure of personal data, the manipulation of statistical findings, the harming of organizations’ reputations, and enormous financial losses for the sporting organization. One or more of the consequences, as previously mentioned, is related to sports organizations and the athletes who are members of those organizations, and they have a direct impact on the corresponding set of sports-related, medical-related, and paramedical enterprises, specifically those that provide specialized sports equipment and associated services. A critical need to detect and quantify threats has long been recognized to better support decision-making when adopting or constructing a safe and reliable sports Internet-of-Things infrastructure, which is becoming increasingly common. Using advanced machine learning algorithms, this research provides a methodology for technology optimization in cybersecurity defenses that is then used in a unique case study utilizing volleyball players to demonstrate its effectiveness. In conjunction with a Monte Carlo optimization technique, an upgraded variant of fuzzy cognitive maps (FCM) is presented in greater detail. This model is utilized for a specific scenario of risk identification of volleyball industry, assessment, and optimization for IoT sports networks.

show abstract

Section: Fcm Methodologymentioning

confidence: 99%

Section: Fcm Methodologymentioning

confidence: 99%

Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Xiao

Chen

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…This is not mathematically correct since from the total number N of concepts, there is a number of concepts whose values remain constant for long period of time when other concepts are changing slowly or fast. In this classical FCM formulation another problem has been that no matter what initial conditions were used, the algorithm was always converted to the same final values [20,24,38,42,48].…”

Section: Advanced Fuzzy Cognitive Maps Modelsmentioning

confidence: 99%

“…The proposed separation facilitates not only the understanding of the system's operation but also the calculation of the concepts' values in their physical nature as the states, inputs and outputs of the real system. Then in a second effort [37][38][39], a revised approach in modelling Fuzzy Cognitive Maps was presented in detail. The new model was composed by the following equations:…”

Section: Advanced Fuzzy Cognitive Maps Modelsmentioning

confidence: 99%

“…In the same paper [48], the sigmoid problem was also studied and a new sigmoid function was proposed which is the same of the study [37] and its equation (11), given above was first proposed by Mpelogianni and Groumpos [37][38][39]. Please note that in classical FCM studies all N concepts remain in one and the same vector.…”

Section: Advanced Fuzzy Cognitive Maps Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Intelligence and Fuzzy Cognitive Maps: Scientific Issues, Challenges and Opportunities

Groumpos¹

2018

STUD INFORM CONTROL

Self Cite

View full text Add to dashboard Cite

Today humankind is facing an unprecedented set of many difficult problems and challenges that have never been encountered before. They urgently need realistic and long lasting solutions. Nowadays, practically all systems have become more and more complex and dynamic in nature. The modelling and controlling challenge of today's complex dynamic systems (CDS) is carefully reviewed. The fuzzy cognitive maps (FCM) approach is used to model CDS. Drawbacks and deficiencies of classical FCMs are identified and solutions to overcome them are mathematically developed. Two basic scientific fields are studied and analyzed on a joint synergistic way for the very first time. Intelligence and intelligent control (IC), cognition and cognitive control (CC) are two challenging scientific fields. A new modelling and control theory is proposed for addressing the complex dynamic systems (CDS).

show abstract