Neuro-fuzzy rule generation: survey in soft computing framework

Mitra, Sushmita; Hayashi, Yuzo

doi:10.1109/72.846746

Cited by 626 publications

(255 citation statements)

References 131 publications

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“…The second problem is addressed by data-driven techniques for identification of fuzzy systems from numerical examples [16], such as the algorithm by Wang and Mendel (W&M) [50,51] and the fuzzy identification algorithm based on clustering by Chiu [12,36], two well established methods among the many alternatives proposed throughout the years [23,19,43,34,2,26,44].…”

Section: Introductionmentioning

confidence: 99%

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Pouzols¹,

Lendasse²,

Barriga³

2010

Fuzzy Sets and Systems

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We propose an automatic methodology framework for short-and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications

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Section: Introductionmentioning

confidence: 99%

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Pouzols¹,

Lendasse²,

Barriga³

2010

Fuzzy Sets and Systems

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show abstract

“…Another interesting approach in this area is the one proposed by del Jesus et al (2004), which applies the idea of boosting (Kearns, 1988) to the evolutionary learning of rule-based classifiers. Neuro-fuzzy methods (Mitra and Hayashi, 2000;Nauck et al, 1997) encode a fuzzy system as a neural network and apply corresponding learning methods (like backpropagation). Fuzzy rules are then extracted from a trained network.…”

Section: Related Workmentioning

confidence: 99%

FURIA: an algorithm for unordered fuzzy rule induction

Hühn

Hüllermeier

2009

Data Min Knowl Disc

382

223

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This paper introduces a novel fuzzy rule-based classification method called FURIA, which is short for Fuzzy Unordered Rule Induction Algorithm. FURIA extends the well-known RIPPER algorithm, a state-of-the-art rule learner, while preserving its advantages, such as simple and comprehensible rule sets. In addition, it includes a number of modifications and extensions. In particular, FURIA learns fuzzy rules instead of conventional rules and unordered rule sets instead of rule lists. Moreover, to deal with uncovered examples, it makes use of an efficient rule stretching method. Experimental results show that FURIA significantly outperforms the original RIPPER, as well as other classifiers such as C4.5, in terms of classification accuracy.

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“…Recently, there has been a growing interest in combining both approaches, and as a result, neurofuzzy computing techniques have been evolved [1,13,14]. ANFIS model combines the neural network adaptive capabilities and the fuzzy logic qualitative nature, which we use as a classifier in the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Vessel Extraction of Conjunctival Images Using LBPs and ANFIS

Zabihi

Pourreza

Banaee

2012

ISRN Machine Vision

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The main goal of medical imaging applications is to diagnose some diseases, try to prevent the progression of them, and actually cure the patients. The number of people that suffer from diabetes is growing very fast these recent years in many countries and it is needed to diagnose this disease in the beginning to prevent the subsequent side effects like blindness and so on. One of the first ways to detect this disease is analysis of vessels in some parts of the eye such as retina and conjunctiva. Some studies have been done on effects of vessel changes of conjunctiva in diabetes diagnosis and it is proved that conjunctival vessel extraction and analysis is a good way for this purpose. In this paper, we proposed a method to detect and extract the vessels of conjunctiva automatically. It is the first stage of the process of diabetes diagnosis. We first extract some textural features from each pixel of the conjunctiva image using LBP and then classify each pixel to vessels or nonvessels according to the features vector based on a supervised classifier, ANFIS. We tested the proposed algorithm on 40 conjunctival images to show the performance and efficiency of our method.

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Neuro-fuzzy rule generation: survey in soft computing framework

Cited by 626 publications

References 131 publications

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

FURIA: an algorithm for unordered fuzzy rule induction

Vessel Extraction of Conjunctival Images Using LBPs and ANFIS

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