Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks

Keller, James M.; Tahani, Hossein

doi:10.1016/0888-613x(92)90018-u

Cited by 98 publications

(22 citation statements)

References 19 publications

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“…In the training phase, the concept of back-propagation is used to minimize the error function (10) where is the number of nodes in layer 5 and and are the target and actual outputs of node in layer 5 for input data . The method for adjusting the learnable weights in layer 4 and the parameters in layer 2 are based on gradient descent search.…”

Section: Learning Of Feature Modulators and Rulesmentioning

confidence: 99%

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Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm

Chakrborty

Pal

2001

IEEE Trans. Syst., Man, Cybern. B

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Abstract-Most methods of fuzzy rule-based system identification (SI) either ignore feature analysis or do it in a separate phase. This paper proposes a novel neuro-fuzzy system that can simultaneously do feature analysis and SI in an integrated manner. It is a five-layered feed-forward network for realizing a fuzzy rule-based system. The second layer of the net is the most important one, which along with fuzzification of the input also learns a modulator function for each input feature. This enables online selection of important features by the network. The system is so designed that learning maintains the nonnegative characteristic of certainty factors of rules. The proposed network is tested on both synthetic and real data sets and the performance is found to be quite satisfactory. To get an "optimal" network architecture and to eliminate conflicting rules, nodes and links are pruned and then the structure is retrained. The pruned network retains almost the same level of performance as that of the original one.

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Section: Learning Of Feature Modulators and Rulesmentioning

confidence: 99%

“…Neural fuzzy systems are fuzzy systems implemented by neural networks [10], [11], [24], [25], [31], [33]. Fuzzy neural systems are neural networks capable of handling fuzzy information [4], [6], [33].…”

Section: Introductionmentioning

confidence: 99%

Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm

Chakrborty

Pal

2001

IEEE Trans. Syst., Man, Cybern. B

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“…A promising strategy is the use of the fuzzy logic, which has been successfully applied for the management of uncertainty and imprecision. Several authors have combined the fuzzy logic with the connectionist approach by different point of view (Hayashi et al, 1993, Ishibuchi et al, 1993, Keller and Tahani, 1992, Pedrycz, 1993. The main purpose of this study is to investigate an architecture in which each node is able to process fuzzy sets.…”

Section: Resultsmentioning

confidence: 99%

Icann ’94

1994

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The use of registered names, trademarks etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use.The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. PrefaceThis book contains the proceedings of the International Conference on Artificial Neural Networks which was held between May 26 and 291994 in Sorrento, organized by the Department of Theoretical Physics of the University of Salerno, IIASS and the Istituto Italiano per gli Studi Filosofici (Napoli), and cosponsored by ENNS, JNNS, IEEE and SIREN. It is the fourth in a series after Helsinki (1991), Brighton (1992) and Amsterdam (1993). The conference is the main event of the European Neural Network Society, which is fostering the growth of the interdisciplinary community ofEuropean researchers and supporting the interaction and cooperation with other international societies in the same scientific area.Neural network is a living proof that the re-birth in science is possible. The first life was driven by a handful of pioneers who, in the fifties and early sixties, discovered the analogies between machines and biological systems as regards communication, control, computing. In a sense, this was not completely new, and we are sure that the mechanical toys of the eighteenth century are a beautiful although naive witness of its illuministic roots. However, the idea was a daring one, and the technological! methodological constraints of the time did not allow it to flourish and trigger a virtuous growth cycle through significant successes in the real world.We are now in the second life. Computation is cheap; software tools are powerful; mathematical tools, developed by computer-aware people, accumulated steadily. Moreover, the brain is less of a black box and can teach us some things. Researchers in many fields have demonstrated in the last decades that they are willing to try again: mathematicians, physicists, psychologists, neuroscientists, and, last but not least engineers who have the crucial task to prove the feasibility of the field and make things work. Of course, there is hype, not all promises will be maintained, but even if only 10% of them succeed, as is happening now in many application areas, this will allow the second wave to expand and consolidate. We can forsee the limits, mainly due to technology, and we can already guess the real breakthroughs, which will make the third wave possible and make neural networks as pervasive as are microcomputers now, will happen when new materials, self-organizing, adaptable wet-ware become available. For all these reasons it is mostly appropriate to dedicate this conference to Eduardo R. Caianiello, one of the pioneers of the first wave and an eminent researcher of the second one. Eduardo R. Caianiello, who was also ...

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“…Fuzzy Set Theory was introduced to deal with vagueness of human judgement, which was oriented to the rationality of uncertainty caused by imprecision or vagueness [35][36][37][38][39]. A major contribution of Fuzzy Set Theory is its capability of representing vague data.…”

Section: Fuzzy Set Theorymentioning

confidence: 99%

Application of a generic bow-tie based risk analysis framework on risk management of sea ports and offshore terminals

Mokhtari

Ren

Roberts

et al. 2011

Journal of Hazardous Materials

125

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Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks

Cited by 98 publications

References 19 publications

Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm

Integrated feature analysis and fuzzy rule-based system identification in a neuro-fuzzy paradigm

Icann ’94

Application of a generic bow-tie based risk analysis framework on risk management of sea ports and offshore terminals

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