Editing Neuro Fuzzy Systems: State-of-the-Art

Title: Neuro Fuzzy Systems: State-of-the-Art

Research Question: How can neuro fuzzy systems be effectively integrated to solve complex problems?

Methodology: The study investigates three types of neuro-fuzzy systems: cooperative, concurrent, and fused models. Each model has its own advantages and disadvantages, which are analyzed and compared.

Results: The research found that fused neuro-fuzzy systems are the most effective as they combine the learning capabilities of both systems. They share data structures and knowledge representations, allowing for better problem solving.

Implications: The findings suggest that fused neuro-fuzzy systems can be applied to a wide range of scientific and engineering areas to solve real-world problems. This integration of ANN and FIS can lead to more accurate and adaptive intelligent systems.

Link to Article: https://arxiv.org/abs/0405011v1
Authors: 
arXiv ID: 0405011v1

[[Category:Computer Science]]
[[Category:Systems]]
[[Category:Neuro]]
[[Category:Fuzzy]]
[[Category:Can]]
[[Category:Fused]]