Dynamical random neural network approach to the traveling salesman problem

Gelenbe, Erol; Koubi, Vassilada; Pekergin, Ferhan

doi:10.1109/icsmc.1993.384945

Cited by 50 publications

(33 citation statements)

References 19 publications

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“…It is shown in [28] that the dynamical RNN yields solutions to the TSP which are close to the optimal in a majority of instances tested. Yet another application is the vertex covering problem, which is designated as NP-complete.…”

Section: Optimizationmentioning

confidence: 99%

“…After the random neural network model (RNN) have introduced by Gelenbe [24,25], it is used for solving several different problems [26,27,28,29,30,31,32,33,34,35,36]. In their studies, Atalay, Gelenbe and Yalabik [31,32] propose a texture generation algorithm using RNN model, and they obtain good results and concluded that RNN can be used to represent and analyze texture.…”

Section: Motivationmentioning

confidence: 99%

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Texture Classification and Retrieval Using the Random Neural Network Model

Teke

Atalay

2006

CMS

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Texture is one of the most important characteristics used in computer vision and image processing applications. In this thesis, a new texture classification and retrieval method is proposed for texture analysis applications. The technique makes use of the random neural network model and it is supervised. The main aim is to represent textures with parameters which are the random neural network weights and classify and retrieve textures using this texture definition. The network has neurons that correspond to each image pixel, and the neurons are connected according to neighboring relationship between pixels. The method is tested on artificial images produced by using Brodatz album and texture blocks cut from remotely sensed imagery.

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Section: Optimizationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Texture Classification and Retrieval Using the Random Neural Network Model

Teke

Atalay

2006

CMS

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“…Some of its other applications can be found in [1,3,4,9,12,25,26,89,[101][102][103]157,174] and several papers reviewing this subject can be found in the papers of the special issue in [49].…”

Section: The Random Neural Networkmentioning

confidence: 99%

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

Caglayan¹

2016

Prob. Eng. Inf. Sci.

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We focus on Erol Gelenbe's scientific and technical contributions to probability models in the computer and information sciences, but limit our survey to the last fifteen years. We start with a brief overview of his work as a single author, as well as his work in collaboration with over 200 co-authors. We discuss some of his recent and innovative work regarding a new probability model that represents Intermittent Energy Sources for Computing and Communications, introducing Energy Packet Networks which are a probabilistic representation of the flow, storage and consumption of electrical energy at the microscopic level (in electronic chips), and at the macroscopic level (e.g. in buildings or data centers) and for its routing and dynamic usage by consuming units (such as computer elements, chips or machines). We next discuss his work on designing computer and communication systems that parsimoniously use energy in order to achieve a satisfactory level of quality of service (QoS). Trade-offs between system QoS and energy consumption are also considered. Then we turn to Prof. Gelenbe's pioneering work on Autonomic Communications and the design and implementation of CPN, the Cognitive Packet Network, and we also briefly review his spiking random neural network that was used in CPN. This is followed by a brief review of work that he conducted since 1999 on human evacuation from dangerous or catastrophic environments, and the design of technology driven Emergency Management Systems. His research since the late 2000s on Gene Regulatory Networks is then covered together with its application to the detecting possible disease from microarray data. Finally, we briefly discuss some novel analytical models that he developed in this period with publications appearing in journals of physics and applied mathematics.

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“…The RNN learning algorithm was applied to video compression (Cramer and Gelenbe [22], Gelenbe et al [141]), to recognize textures (Gelenbe and Feng [79]), and tumors in magnetic resonance images of the human brain (Gelenbe, Feng, and Krishnan [80]). Other applications of the random neural network that do not require learning include function optimization (Gelenbe, Koubi, and Pekergin [99]) and texture generation (Atalay and Gelenbe [9], Atalay, Gelenbe, and Yalabik [10]). Applications of the RNN were published for video compression (Cramer, Gelenbe, and Bakircioglu [20,21]), complex recognition tasks (Abdelbaki, Gelenbe, and El-Khamy [1], Abdelbaki, Gelenbe, and Kocak [2], Abdelbaki et al [3], Aguilar and Gelenbe [8], Gelenbe, Ghanwani, and Srinivasan [85], Hocaoglu et al [155]), and to the sensory search of patterns and objects (Gelenbe and Cao [74], Gelenbe and Koçak [97], Gelenbe, Koçak, and Wang [98]).…”

Section: Extensions and Applications Of The Random Neural Network (Rnn)mentioning

confidence: 99%

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

Caglayan¹

2017

Prob. Eng. Inf. Sci.

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This paper introduces a special issue of this journal (Probability in the Engineering and Informational Sciences) that is devoted to G(elenbe)-Networks and their Applications. The special issue is based on revised versions of some of the papers that were presented at a workshop held in early January 2017 at the Séminaire Saint-Paul in Nice (France). It includes contributions in several research directions that followed from the introduction of the G-Network in the late 1980s. The papers present original theoretical developments, as well as applications of G-Networks to Machine Learning, to the performance optimization of energy systems via the novel Energy Packet Networks formalism for systems that operate with renewable and intermittent energy sources, and to packet network routing and Cloud management over the Internet. We introduce these contributions from the perspective of an overview of recent work based on G-Networks.

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Dynamical random neural network approach to the traveling salesman problem

Cited by 50 publications

References 19 publications

Texture Classification and Retrieval Using the Random Neural Network Model

Texture Classification and Retrieval Using the Random Neural Network Model

Erol Gelenbe: A Career in Multi-Disciplinary Probability Models

G-Networks and Their Applications to Machine Learning, Energy Packet Networks and Routing: Introduction to the Special Issue

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