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Network with Sub-Networks [article]

Ninnart Fuengfusin, Hakaru Tamukoh
2019 arXiv   pre-print
We introduce network with sub-networks, a neural network which its weight layers could be detached into sub-neural networks during inference.  ...  To develop weights and biases which could be inserted in both base and sub-neural networks, firstly, the parameters are copied from sub-model to base-model.  ...  Acknowledgements This research was supported by JSPS KAKENHI Grant Numbers 17K20010.  ... 
arXiv:1908.00763v2 fatcat:32orjpqpirfbvjxh4wzbo2uzxi

Network with Sub-networks: Layer-wise Detachable Neural Network

Ninnart Fuengfusin, Hakaru Tamukoh
2020 Journal of Robotics, Networking and Artificial Life (JRNAL)  
In this paper, we introduce a network with sub-networks: a neural network whose layers can be detached into sub-neural networks during the inference phase.  ...  With the Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets, our base-model achieves identical test-accuracy to that of regularly trained models.  ...  ACKNOWLEDGMENT This research was supported by JSPS KAKENHI Grant Numbers 17K20010.  ... 
doi:10.2991/jrnal.k.201215.006 fatcat:dfnpyehjdjarxfh3po6t2yrkki

Associative Neural Network [chapter]

Igor V. Tetko
2008 Msphere  
An associative neural network has a memory that can coincide with the training set.  ...  An associative neural network (ASNN) is a combination of an ensemble of the feed-forward neural networks and the K-nearest neighbor technique.  ...  predict the test cases with neural networks.  ... 
doi:10.1007/978-1-60327-101-1_10 pmid:19065811 fatcat:xkdbnug7fvaqzgkoayjz5r6o3y

A network of networks processing model for image regularization

Ling Guan, J.P. Anderson, J.P. Sutton
1997 IEEE Transactions on Neural Networks  
Index Terms-Network of networks, image regularization, image and pattern analysis, adaptive control parameters, learning.  ...  Both forms are readily implemented using an NoN architecture.  ...  A more reasonable approach is to assign each cluster an optimal The procedure is to set up rectangular clusters, and after assigning a value to each cluster, the network is run freely based on some predefined  ... 
doi:10.1109/72.554202 pmid:18255621 fatcat:k35smrcxbnbqbob7xq3rqdxrda

Robotic UBIquitous COgnitive Network [chapter]

Giuseppe Amato, Mathias Broxvall, Stefano Chessa, Mauro Dragone, Claudio Gennaro, Rafa López, Liam Maguire, T. Martin Mcginnity, Alessio Micheli, Arantxa Renteria, Gregory M. P. O'Hare, Federico Pecora
2012 Ambient Intelligence - Software and Applications  
This paper briefly illustrates how these techniques are being extended, integrated, and applied to AAL applications.  ...  The EU FP7 project RUBICON develops self-sustaining learning solutions yielding cheaper, adaptive and efficient coordination of robotic ecologies.  ...  This work is partially supported by the EU FP7 RUBICON project (contract n. 269914)  ... 
doi:10.1007/978-3-642-28783-1_23 dblp:conf/isami/AmatoBCDGLMMMROP12 fatcat:5pp3zpl2hvfm7iz43cnflchbbu

On Adaptive Networks and Network Reification [chapter]

Jan Treur
2019 Studies in Systems, Decision and Control  
Finally, it is discussed how mathematical analysis of emerging behavior of a network not only can be applied to non-adaptive base networks, but also to reified adaptive networks.  ...  It is pointed out how repeated application of network reification can be used to model adaptive networks with the adaptation of multiple orders.  ...  for the dynamics within the base network (2) an adaptation model for the dynamics of the network structure characteristics of the base network.  ... 
doi:10.1007/978-3-030-31445-3_1 fatcat:rugzcosuzrdv7dd3k7yjp6arcm

Output feedback control of nonlinear systems using RBF neural networks

S. Seshagiri, H.K. Khalil
2000 IEEE Transactions on Neural Networks  
An RBF neural network is used to adaptively compensate for the plant nonlinearities. The network weights are adapted using a Lyapunov-based design.  ...  An adaptive output feedback control scheme for the output tracking of a class of continuous-time nonlinear plants is presented.  ...  CONCLUSIONS An adaptive output feedback scheme that uses RBF neural networks has been studied for the control of a class of nonlinear systems represented by input-output models.  ... 
doi:10.1109/72.822511 pmid:18249740 fatcat:j6qadwsaszaipazmutvw76tkci

Anomaly based Intrusion Detection using Neural Networks in 5G Network

Thota Guna Durga Prashanth
2021 International Journal for Research in Applied Science and Engineering Technology  
SDS will tackle momentum progresses in AI to plan a CNN (Convolutional Neural Network) utilizing NAS (Neural Architecture Search) to distinguish irregular organization traffic.  ...  SDS can be applied to an interruption location framework to make a more proactive and start to finish protection for a 5G organization.  ...  Neural engineering search can be characterized as an angle based technique for finding advanced models.  ... 
doi:10.22214/ijraset.2021.38691 fatcat:dxkz457jerdxbb5z6y653bgbfi

Artificial neural networks

J.J. Hopfield
1988 IEEE Circuits & Devices  
For these reasons, this syllabus is confined to a broad view of the field of artificial neural networks.  ...  Control Neural networks are nonlinear, adaptive elements. They have found, therefore, immediate scope for application in nonlinear, adaptive control.  ...  The problem is that there is not a particular method for doing that, and it is matter of ingenuity. fig. A3.4 ) , and in this case we speak of a limit cycle, or can have other forms.  ... 
doi:10.1109/101.8118 fatcat:c6royelcdzfethnmmur2tj524i

AI-Native Network Slicing for 6G Networks [article]

Wen Wu, Conghao Zhou, Mushu Li, Huaqing Wu, Haibo Zhou, Ning Zhang, Xuemin Shen, Weihua Zhuang
2021 arXiv   pre-print
AI-based solutions are first discussed across network slicing lifecycle to intelligently manage network slices, i.e., AI for slicing.  ...  Finally, a case study is presented, followed by a discussion of open research issues that are essential for AI-native network slicing in 6G networks.  ...  The AI for slicing is to help reduce network management complexity, while adapting to dynamic network environments by exploiting the capability of AI in network slicing.  ... 
arXiv:2105.08576v2 fatcat:3nwd256mhffe3nskky5faz3aci

Change Detection with Weightless Neural Networks

Massimo De Gregorio, Maurizio Giordano
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops  
In this paper a pixel-based Weightless Neural Network (WNN) method to face the problem of change detection in the field of view of a camera is proposed.  ...  The WNN approach is very simple and straightforward, and it gives high rank results in competition with other approaches applied to the 2014 benchmark dataset.  ...  For this reason we implemented an OpenMP C++ version of CwisarDH to better exploit parallelism on a multicore CPUs.  ... 
doi:10.1109/cvprw.2014.66 dblp:conf/cvpr/GregorioG14 fatcat:g3pnficlc5dctid5jbunkw5gsm

Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks [article]

Francesc Wilhelmi, Marc Carrascosa, Cristina Cano, Anders Jonsson, Vishnu Ram, Boris Bellalta
2021 arXiv   pre-print
In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network.  ...  Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network.  ...  With knowledge on supported capabilities, the simulated functionalities can be adapted to the use case.  ... 
arXiv:2005.08281v2 fatcat:4g7zchvkazakbjuvzy62h37qsa

A Framework for Embedded Hypercube Interconnection Networks: Based on Neural Network Approach

Mohd. KalamuddinAhmad, Mohd. Husain, A.A. Zilli
2015 International Journal of Computer Applications  
This paper is concerned with routing of data in an embedded hypercube interconnection using the approach based on neural net architecture.  ...  In this paper we first show that n dimensional hypercube can be embedded in layer neural layer network such that for any node of hypercube, its neighboring nodes of other layer are evenly partition into  ...  Y i = f (a) NEURAL NETWORKS BASED A FRAMEWORK FOR EMBEDDED INTERCONNECTIO NETWORK (c) A Feedforword 3-3-3-3 Network (without embedded hypercube) A FEEDFORWORD-NETWORK WITH EMBEDDED HYPERCUBE: The  ... 
doi:10.5120/21201-3873 fatcat:2qprdoc47jbntmzguipstvc7ke

An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources

A.D. Doulamis, N.D. Doulamis, S.D. Kollias
2003 IEEE Transactions on Neural Networks  
In this paper, an adaptable neural-network architecture is proposed covering both cases.  ...  For this reason, traffic characterization and modeling of such services are required for an efficient network operation.  ...  The proposed scheme is based on an efficient recursive estimation of neural-network weights for adapting network output to current conditions.  ... 
doi:10.1109/tnn.2002.806645 pmid:18237998 fatcat:x7lwm4pxbvgl7h3cyobxnefgzm

Causal connectivity of evolved neural networks during behavior

Anil K. Seth
2005 Network  
In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory  ...  This method, called causal connectivity analysis, is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment  ...  Acknowledgments This research was made possible by the Neurosciences Research Foundation, which supports the work of The Neurosciences Institute.  ... 
doi:10.1080/09548980500238756 pmid:16350433 fatcat:gb4w7wzrr5fxxlfznawwlchjja
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