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}
}
  • A. Abraham
  • Published 13 June 2001
  • Computer Science
  • ArXiv
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… 
Adaptation of Fuzzy Inference System Using Neural Learning
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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 ?
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
TLDR
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
TLDR
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
  • A. Abraham
  • Computer Science
    Proceedings of the IEEE Internatinal Symposium on Intelligent Control
  • 2002
TLDR
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
TLDR
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.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 16 REFERENCES
Evolutionary Design of Neuro-Fuzzy Systems - A Generic Framework
TLDR
This paper attempts to formulate a 5-tier hierarchical evolutionary search procedure for the optimal design of NF systems and describes each of the evolutionary search procedures in detail and the interactions among them.
Dynamic evolving fuzzy neural networks with `m-out-of-n' activation nodes for on-line adaptive systems
TLDR
It is proved that the mEFuNNs can effectively learn complex temporal sequences in an adaptive way and outperform EFunns, ANFIS and other neural network and hybrid models.
A neuro-fuzzy approach to obtain interpretable fuzzy systems for function approximation
  • D. Nauck, R. Kruse
  • Computer Science
    1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228)
  • 1998
TLDR
The NEFPROX model, which is discussed in this paper is more general, and it can be used for any problem based on function approximation, especially the problem to obtain interpretable fuzzy systems by learning.
Neural-Network-Based Fuzzy Logic Control and Decision System
TLDR
A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed, in the form of feedforward multilayer net, which avoids the rule-matching time of the inference engine in the traditional fuzzy logic system.
FUN: optimization of fuzzy rule based systems using neural networks
A method for optimization of fuzzy rule based systems using neural networks is described. A neural network model with special neurons has been developed so that the translation of fuzzy rules and
Neuro-Fuzzy Systems
TLDR
This paper tries to give the term neuro-fuzzy meaning in the context of three applications of fuzzy systems, which are fuzzy control, fuzzy classification, and fuzzy function approximation.
Learning and tuning fuzzy logic controllers through reinforcements
TLDR
The generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; and learns to produce real-valued control actions.
An online self-constructing neural fuzzy inference network and its applications
TLDR
A linear transformation for each input variable can be incorporated into the network so that much fewer rules are needed or higher accuracy can be achieved.
Neuro-fuzzy systems for function approximation
TLDR
A neuro-fuzzy architecture for function approximation based on supervised learning that is an extension to the already published NEFCON and NEFCLASS models and can be used for any application based on function approximation.
Deep combination of fuzzy inference and neural network in fuzzy inference software - FINEST
TLDR
An important concept is described, a deep combination of the fuzzy inference and the neural network in FINEST, which enables FINEST to tune the inference method itself.
...
1
2
...