<i>Neural Networks and Fuzzy Systems</i>

Kosko, Bart; Burgess, John C.

doi:10.1121/1.423096

Cited by 1,028 publications

(326 citation statements)

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“…Hence the value defines a threshold below which no committed category choice for is searched. In other words, a committed category choice for can be searched only if it satisfies (13) It is apparent from (11) that the order in which categories are searched is fixed and independent of the vigilance level Categories of identical size are searched in increasing order of their distance from , that is, closest first and farthest last, while categories that are equally distant from are searched in increasing order of size, that is, smallest first and largest last. In the particular case where the weights are not changing, for all the selected categories.…”

Section: Next Supposementioning

confidence: 99%

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Generalization, discrimination, and multiple categorization using adaptive resonance theory

Lavoie¹,

Crespo²,

Savaria

1999

IEEE Trans. Neural Netw.

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Abstract-The internal competition between categories in the adaptive resonance theory (ART) neural model can be biased by replacing the original choice function by one that contains an attentional tuning parameter under external control. For the same input but different values of the attentional tuning parameter, the network can learn and recall different categories with different degrees of generality, thus permitting the coexistence of both general and specific categorizations of the same set of data. Any number of these categorizations can be learned within one and the same network by virtue of generalization and discrimination properties. A simple model in which the attentional tuning parameter and the vigilance parameter of ART are linked together is described. The self-stabilization property is shown to be preserved for an arbitrary sequence of analog inputs, and for arbitrary orderings of arbitrarily chosen vigilance levels.

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Section: Next Supposementioning

confidence: 99%

“…For this work it is convenient to rewrite it as follows: (11) where (12) denotes Kosko's fuzzy Hamming distance, that is, [13]. (Note that (12) defines distance between hyperrectangles, whereas in [4] distance is defined between points).…”

Section: Fuzzy Artmentioning

confidence: 99%

Generalization, discrimination, and multiple categorization using adaptive resonance theory

Lavoie¹,

Crespo²,

Savaria

1999

IEEE Trans. Neural Netw.

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“…the state vector remains as (1, 0, 0… 0, 1). This state vector (1, 0, 0… 0, 1) is called the fixed point [4]. Definition 2.12: Finite number of FCMs can be combined together to produce the joint effect of all the FCMs.…”

Section: Preliminariesmentioning

confidence: 99%

A Study on Problems Faced by the People with Disabilities (PWDs) Using Combined Overlap Block Fuzzy Cognitive Maps (COBFCMS)

Prakash¹,

Rajkumar²,

Praveena³

2013

IJCTE

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Abstract-The Imperative reasons for problems faced by the people with Disabilities (PWDs) using combined overlap block fuzzy cognitive maps (COBFCMS) defined by W.B Vasantha Kandasamy is analyzed in this paper. The combined overlap block FCM's defined in this method become effective when the number of concepts can be grouped and are large in numbers. In this paper we analyzed the problems and are large in number. In this paper we analyzed the problem and find out the reasons how it has happened to people. This paper has six sections, first section gives the information about development of fuzzy cognitive maps, second section gives preliminaries of fuzzy cognitive maps, and combined overlap block fuzzy cognitive maps, in section three we explain about the problem faced by PWDs, in section four we explain the method of determining their hidden pattern, in section five, we give the concept of the problem, final section gives the conclusion based on our studies. Index Terms-FCMS, COBFCMS, disablity.

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“…They are neuro-fuzzy systems and they have the ability to incorporate human knowledge and to adapt their knowledge base via new optimization techniques. Thus there are likely to play an increasingly important role in the conception and design of hybrid intelligent systems for discipline scientific applications [5] [6].…”

Section: Fuzzy Cognitive Mapsmentioning

confidence: 99%

The challenge of using soft computing for decision support during labour

Stylios¹,

Georgoulas²,

Groumpos³

2001

PsycEXTRA Dataset

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Abstract-This paper presents the development of a Decision Support System for managing the labour based on the Soft Computing technique of Fuzzy Cognitive Maps. During labour, obstetricians manage and take decisions on the fetus delivery and the procedure of the labour, monitoring continuously a visual recording of the fetus heart signal and uterine contractions and they also take under consideration other physiological medical measurements and factors. Obstetricians utilize their experience and they decide either to proceed to a natural labour or to a Caesarean section. The developing system for managing labour delivery will combine evaluation of cardiotocographic signal with other physiological data in order to create an advanced Decision Support System.

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Neural Networks and Fuzzy Systems

Cited by 1,028 publications

References 0 publications

Generalization, discrimination, and multiple categorization using adaptive resonance theory

Generalization, discrimination, and multiple categorization using adaptive resonance theory

A Study on Problems Faced by the People with Disabilities (PWDs) Using Combined Overlap Block Fuzzy Cognitive Maps (COBFCMS)

The challenge of using soft computing for decision support during labour

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