Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Using fuzzy numbers in educational grading system
Fuzzy Sets and Systems
Fuzzy Control and Fuzzy Systems
Fuzzy Control and Fuzzy Systems
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
A GA-based fuzzy modeling approach for generating TSK models
Fuzzy Sets and Systems - Modeling and control
A neural fuzzy system with fuzzy supervised learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy set approach to the assessment of student-centered learning
IEEE Transactions on Education
Design of fuzzy systems using neurofuzzy networks
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Transactions on Neural Networks
Functional equivalence between radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
Computers and Industrial Engineering
Application of adaptive network based fuzzy inference system method in economic welfare
Knowledge-Based Systems
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
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This paper introduces a systematic approach for the design of a fuzzy inference system based on a class of neural networks to assess the students' academic performance. Fuzzy systems have reached a recognized success in several applications to solve diverse class of problems. Currently, there is an increasing trend to expand them with learning and adaptation capabilities through combinations with other techniques. Fuzzy systems-neural networks and fuzzy systems-genetic algorithms are the most successful applications of soft computing techniques with hybrid characteristics and learning capabilities. The developed method uses a fuzzy system augmented by neural networks to enhance some of its characteristics like flexibility, speed, and adaptability, which is called the adaptive neuro-fuzzy inference system (ANFIS). New trends in soft computing techniques, their applications, model development of fuzzy systems, integration, hybridization and adaptation are also introduced. The parameters set to facilitate the hybrid learning rules for the constitution of the Sugeno-type ANFIS architecture is then elaborated. The method can produce crisp numerical outcomes to predict the student's academic performance (SAP). It also provides an alternative solution to deal with imprecise data. The results of the ANFIS model are as robust as those of the statistical methods, yet they encourage a more natural way to interpret the student's outcomes.