# Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques

@article{Abraham2001NeuroFS, title={Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques}, author={Ajith Abraham}, journal={ArXiv}, year={2001}, volume={cs.AI/0405011} }

Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN and…

## 233 Citations

Adaptation of Fuzzy Inference System Using Neural Learning

- Computer Science
- 2005

Three different types of cooperative neurofuzzy models namely fuzzy associative memories, fuzzy rule extraction using self-organizing maps and systems capable of learning fuzzy set parameters are presented.

Beyond integrated neuro-fuzzy systems: reviews, prospects, perspectives and directions

- Computer Science
- 2001

This paper presents some short fundamental concepts and modeling aspects of neuro-fuzzy systems emphasizing on Takagi Sugeno and Mamdani fuzzy inference system and an attempt to throw light on future research directions and some of the problems to be addressed that go beyond the current neuro- fuzzy algorithms.

Recent advances in neuro-fuzzy system: A survey

- Computer ScienceKnowl. Based Syst.
- 2018

A review of different neuro-fuzzy systems based on the classification of research articles from 2000 to 2017 is proposed to help readers have a general overview of the state-of-the-arts of neuro- fizzy systems and easily refer suitable methods according to their research interests.

Fuzzy Logic And Neuro-Fuzzy Modeling

- Computer Science
- 2012

The theory behind basic fuzzy logic is described and the potential limitations of this method will be described as this provides the reader with a greater understanding of how the techniques can be applied.

Design of experiments in neuro-fuzzy systems

- Computer ScienceFifth International Conference on Hybrid Intelligent Systems (HIS'05)
- 2005

A statistical analysis is performed to verify the interactions and interrelations between parameters in the design of neuro-fuzzy systems and finds that adaptive neuro fuzzy inference system and evolving fuzzy neural networks are effective systems.

Cerebral Quotient of Neuro Fuzzy Techniques – Hype or Hallelujah ?

- 2001

Fuzzy inference systems and neural networks are complementary technologies in the design of adaptive intelligent systems. Artificial Neural Network (ANN) learns from scratch by adjusting the…

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

- Computer ScienceWiley Interdiscip. Rev. Data Min. Knowl. Discov.
- 2019

Special focus is given to Genetic Programming‐based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature.

Chapter 15 Hybridizing Neural and Fuzzy Systems

- Computer Science
- 2010

This chapter would present how the problem modeling capabilities of the fuzzy systems combines with the learning ability of the neural networks to create the Adaptive Neuro Fuzzy Inference Systems.

EvoNF: a framework for optimization of fuzzy inference systems using neural network learning and evolutionary computation

- Computer ScienceProceedings of the IEEE Internatinal Symposium on Intelligent Control
- 2002

This paper explores how the optimization of fuzzy inference systems can be further improved using a meta-heuristic approach combining neural network learning and evolutionary computation and presents the theoretical frameworks and some experimental results to demonstrate the efficiency of the proposed technique.

A Neuro-Fuzzy Decision Support System for Selection of Small Scale Business

- Computer ScienceIPMU
- 2010

This article describes four approaches of neuro-fuzzy systems with their broad design and also presents general structure of a business advisory system using hybrid neuro- fuzzy approach.

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