Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A course in fuzzy systems and control
A course in fuzzy systems and control
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
Soft Computing and Its Applications
Soft Computing and Its Applications
A Fuzzy Approach to Digital Image Warping
IEEE Computer Graphics and Applications
Tools of soft computing as applied to the problem of facilities layout planning
IEEE Transactions on Fuzzy Systems
Soft computing for greenhouse climate control
IEEE Transactions on Fuzzy Systems
Dynamical cognitive network - an extension of fuzzy cognitive map
IEEE Transactions on Fuzzy Systems
Linguistic modeling by hierarchical systems of linguistic rules
IEEE Transactions on Fuzzy Systems
From approximative to descriptive fuzzy classifiers
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
In order for Fuzzy Systems to continue to flourish in all scientific research areas, not only engineering, a more efficient and effective way of transmitting the relevant knowledge and skills of this discipline is necessary. Conventional tutorials in this area follow a fixed outline starting from the basic principles (set operations, relations, etc) and ending with current applications such as pattern recognition, information classification and system control. We argue that an iterative and concurrent top-down/down-top approach to learning would be more effective, and suggest that a plan to discover concepts first and map them afterwards is viable. Several recent publications and a case study currently under development in the field of medicine are used as examples to show evidence for the future of this new approach.