The Random Neural Network Model for Texture Generation

Atalay, Volkan; GELENBE, EROL; Yalabik, Nese

doi:10.1142/9789812797889_0006

Cited by 11 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its main analytical properties are the “product form” and the existence of the unique network steady-state solution. It has been applied in different applications including search for exit routes for evacuees in emergency situations [ 28 , 29 ], pattern-based search for specific objects [ 30 ], video compression [ 31 ], and image texture learning and generation [ 32 ].…”

Section: Deep Learning Cluster Structures For Management Decisionsmentioning

confidence: 99%

Deep Learning Cluster Structures for Management Decisions: The Digital CEO

Serrano

2018

Sensors

View full text Add to dashboard Cite

This paper presents a Deep Learning (DL) Cluster Structure for Management Decisions that emulates the way the brain learns and makes choices by combining different learning algorithms. The proposed model is based on the Random Neural Network (RNN) Reinforcement Learning for fast local decisions and Deep Learning for long-term memory. The Deep Learning Cluster Structure has been applied in the Cognitive Packet Network (CPN) for routing decisions based on Quality of Service (QoS) metrics (Delay, Loss and Bandwidth) and Cyber Security keys (User, Packet and Node) which includes a layer of DL management clusters (QoS, Cyber and CEO) that take the final routing decision based on the inputs from the DL QoS clusters and RNN Reinforcement Learning algorithm. The model has been validated under different network sizes and scenarios. The simulation results are promising; the presented DL Cluster management structure as a mechanism to transmit, learn and make packet routing decisions is a step closer to emulate the way the brain transmits information, learns the environment and takes decisions.

show abstract

Section: Deep Learning Cluster Structures For Management Decisionsmentioning

confidence: 99%

Deep Learning Cluster Structures for Management Decisions: The Digital CEO

Serrano

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…It was originally introduced in [9] and extended in [10][11][12][13][14][15]. Although the RNN model was initially inspired by biophysical neural networks, it has been successfully applied in many areas such as associative memory [16][17][18], image processing [19][20][21], texture generation [22,23], video QoS and compression [19,[24][25][26][27][28][29], as well as task assignment [30] and resource allocation [31], and has inspired the use of negative customers in queuing networks, which has led to G-networks [32][33][34][35][36].…”

Section: Rnn-based Algorithmsmentioning

confidence: 99%

The Cognitive Packet Network: A Survey

Sakellari

2009

The Computer Journal

View full text Add to dashboard Cite

Current and future multimedia networks require connections under specific quality of service (QoS) constraints which can no longer be provided by the best-effort Internet. Therefore, 'smarter'networks have been proposed in order to cover this need. The cognitive packet network (CPN) is a routing protocol that provides QoS-driven routing and performs self-improvement in a distributed manner, by learning from the experience of special packets, which gather on-line QoS measurements and discover new routes. The CPN was first introduced in 1999 and has been used in several applications since then. Here we provide a comprehensive survey of its variations, applications and experimental performance evaluations.

show abstract

“…The RNN has many engineering applications: it represents image textures [9] to obtain accurate segmentation of brain Magnetic Resonance Images [71]. It achieves high video compression ratios at low computational cost [26,27,72], and has been used for vehicle classification [5], the estimation of Quality of Experience (QoE) [120], and real-time cognitive radio deployment [6].…”

Section: Introductionmentioning

confidence: 99%

Random Neural Network Methods and Deep Learning

Yin¹

2019

Prob. Eng. Inf. Sci.

View full text Add to dashboard Cite

The random neural network (RNN) is a mathematical model for an “integrate and fire” spiking network that closely resembles the stochastic behavior of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning. Recently, the properties of the RNN for DL have been investigated, in order to combine their power. Recent results demonstrate that the gap between RNNs and DL can be bridged and the DL tools based on the RNN are faster and can potentially be used with less energy expenditure than existing methods.

show abstract

The Random Neural Network Model for Texture Generation

Cited by 11 publications

References 0 publications

Deep Learning Cluster Structures for Management Decisions: The Digital CEO

Deep Learning Cluster Structures for Management Decisions: The Digital CEO

The Cognitive Packet Network: A Survey

Random Neural Network Methods and Deep Learning

Contact Info

Product

Resources

About