A neuro-fuzzy system for inferencing

Pal, Krishan; Pal, Nikhil R.

doi:10.1002/(sici)1098-111x(199911)14:11<1155::aid-int5>3.0.co;2-v

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…A typical combination of these two techniques is the so-called neuro-fuzzy control, which is basically a fuzzy control augmented by neural networks to enhance its characteristics like flexibility, data processing capability, and adaptability [17], [63], [72], [90], [123], [124], [138], [163], [177], [178], [186], [187], [193], [205], [209], [217], [271], [294], [305], [306], [342]. The process of fuzzy reasoning is realized by neural networks, whose connection weights correspond to the parameters of fuzzy reasoning [38], [123], [124], [135], [187], [220], [231], [232], [264]. Using back-propagation type, or reinforcement type, or any other type neuro network learning algorithms, a neuro-fuzzy control system can identify fuzzy control rules and learn (tune) membership functions of the fuzzy reasoning, and thus realize the neuro-fuzzy control.…”

Section: Neuro-fuzzy Controlmentioning

confidence: 99%

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Feng

2006

IEEE Trans. Fuzzy Syst.

1,510

629

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Section: Neuro-fuzzy Controlmentioning

confidence: 99%

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Feng

2006

IEEE Trans. Fuzzy Syst.

1,510

629

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“…This is extended in Ref. [108] by using neural learning to find an optimal relation representing a set of fuzzy compositional rules of inference.…”

Section: Designing Neural Net By Fuzzy Logic Formalismmentioning

confidence: 99%

Neuro-fuzzy rule generation: survey in soft computing framework

Mitra

Hayashi

2000

IEEE Trans. Neural Netw.

628

235

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The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.

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“…Neuro-Fuzzy networks have also been proposed to generate inference rules and to tune the membership functions utilizing numerical control data [13]. Several approaches have been discussed to cluster an inputoutput space to generate fuzzy if-then rules [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Modeling Connectionist Neuro-Fuzzy network and Applications

Shalinie¹

2005

Neural Comput & Applic

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The main objective of this paper is to propose a Neuro-Fuzzy network, which can model a system from input-output data by automatically dividing the inputoutput space and extracting fuzzy if-then rules from numerical data. The structure of the network is simple with input, rule and output layers only. The connections between input and rule layer is used to derive the membership functions of the fuzzified part. Kohonen's self-organizing learning algorithm is applied to partition the pattern space. Using this algorithm, similar rules are mapped close by and extraction of if-then rules is made easy. It can also adapt to a number of rules automatically. The proposed network is verified for three benchmark applications. Experimental results show that the adaptive method discussed in this paper not only trains in a few hundred iterations but also provides better performance measures when compared to conventional methods.

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A neuro-fuzzy system for inferencing

Cited by 6 publications

References 15 publications

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Neuro-fuzzy rule generation: survey in soft computing framework

Modeling Connectionist Neuro-Fuzzy network and Applications

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