Fuzzy neural control of voice cells in ATM networks

Ndousse, T.D.

doi:10.1109/49.339916

Cited by 64 publications

(12 citation statements)

References 9 publications

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“…In particular, we have a finite FAM if the fuzzy sets are defined on a finite chain. Applications of FAMs include backing up a truck and trailer, target tracking, voice cell control in ATM networks, and forecasting the average monthly streamflow of a large hydroelectric plant [26,29,39,58,[60][61][62]. A comprehensive survey on FAMs can be found in [62].…”

Section: Basic Concepts Of Fuzzy Associative Memoriesmentioning

confidence: 99%

“…Here, I p denotes the residual implication associated with the t-norm T p that is used in (39). Recall that the residual implication I p can be computed either by (34) or (36).…”

Section: Lattice Framework For P-ifamsmentioning

confidence: 99%

“…. , k}, the synaptic weight matrix V 2 L mÂn and the threshold vector m 2 L m of a p-IFAM that satisfies (39) can be determined by means of the following equation:…”

Section: Lattice Framework For P-ifamsmentioning

confidence: 99%

“…The former computes the maximum of minimums while a max-product FAM computes the maximum of products. Despite successful applications of these FAM models to problems such as backing up a truck and trailer, target tracking, and voice cell control in ATM networks, they suffer from an extremely low storage capacity of one rule per FAM matrix [29,39]. In order to overcome the severe limitations of Kosko's FAMs in storage capacity, several researchers have developed improved FAM versions that are capable of storing multiple pairs of fuzzy patterns.…”

Section: Introductionmentioning

confidence: 99%

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Permutation-based finite implicative fuzzy associative memories

Valle

2010

Information Sciences

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Section: Basic Concepts Of Fuzzy Associative Memoriesmentioning

confidence: 99%

“…Here, I p denotes the residual implication associated with the t-norm T p that is used in (39). Recall that the residual implication I p can be computed either by (34) or (36).…”

Section: Lattice Framework For P-ifamsmentioning

confidence: 99%

“…. , k}, the synaptic weight matrix V 2 L mÂn and the threshold vector m 2 L m of a p-IFAM that satisfies (39) can be determined by means of the following equation:…”

Section: Lattice Framework For P-ifamsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Permutation-based finite implicative fuzzy associative memories

Valle

2010

Information Sciences

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“…A UPC mechanism particularly designed for policing voice sources has been proposed by Ndousse (1994). Since voice cells are characterized by a high degree of burstiness, utilization of a classical control approach faces difficulties.…”

Section: Traffic Policing Usage Parameter Controlmentioning

confidence: 99%

A roadmap of fuzzy logic in ATM, mobile and active networks: challenges of the new century

Vasilakos

2003

Soft Computing - A Fusion of Foundations, Methodologies and App

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ATM networking technology was conceived 20 years ago, and installations will reach their peak the next 10 years. Active networks will be the technology which will follow the ATM. In this article, we address the most important issues regarding recent advances and future perspectives in ATM, including IP/ATM integration, Active Networks, MobileActive Networks and the impact of fuzzy technology in solving the important problems of the above future networking technologies. In the new century it is imperative that we shift from a technology to an application (needs of consumers) focus, where ubiquitous and invisible (context-aware) computing will be a reality.

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Intelligent Flow Control under a Game Theoretic Framework

Chen

2001

Telecommunications Optimization: Heuristic and Adaptive Techniques

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Each of us is interested in optimization, and telecommunications. Via several meetings, conferences, chats, and other opportunities, we have discovered these joint interests and decided to put together this book.It certainly wasn't easy. What made it difficult is partly the reason for this book in the first place: things are moving fast! As researchers in both industry and academia, we are under constant pressure to keep up with new technologies and opportunities. Part of our day jobs involves developing good algorithms for a steady surge of new problems arising from industry. Another part of our day jobs involves finding realistic problems which might yield to some of the algorithms we have developed, which we particularly like, but which don't work on anything else. The remainder of our day jobs tends to involve lecturing (for two of us), project management (for all of us), and things like exam-setting and marking. Finally, for two of us, childcare takes up a substantial part of our night jobs. Naturally, the book was finished when we worked out how to stuff 46 hours into a single day, and keep that up for a few successive weeks.Seriously, though, telecommunicatons is growing and changing very quickly, and all of this involves the increasing availability of new real-world problems for optimization researchers to address. Telecommunications engineers tend to know all about the problems involved, but are often not aware of developments in computer science and artificial intelligence which might go a long way towards solving those problems. We therefore argue, at the beginning of Chapter 1, that there needs to be more collaboration between the communities, so that both sides can be equipped for the challenges ahead. Indeed, this book is the result of one such collaboration.We have oriented it slightly towards the telecommunications engineer who knows little about the computational techniques, rather than the other way around. This is mainly because it is much easier to describe the techniques than it is to describe the telecommunications issues; the latter is a much larger collection. However, individual chapters go into some detail, often at an introductory level, in describing particular kinds of problem. Also, we provide a brief introduction to the essence of the problems at the beginning of each part.Let us know what you think. In particular, if you are a telecommunications engineer, and have been inspired by a chapter or two in this book to try out some techniques that you otherwise wouldn't have, please get in touch: your story may make it into the next edition!

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Fuzzy neural control of voice cells in ATM networks

Cited by 64 publications

References 9 publications

Permutation-based finite implicative fuzzy associative memories

Permutation-based finite implicative fuzzy associative memories

A roadmap of fuzzy logic in ATM, mobile and active networks: challenges of the new century

Intelligent Flow Control under a Game Theoretic Framework

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