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Distance-Based Sparse Associative Memory Neural Network Algorithm for Pattern Recognition

Lei Chen, Songcan Chen
2006 Neural Processing Letters  
By means of the approach, the two new associative memory neural networks, i.e., distance-based sparse ECAM (DBS-ECAM) and distance-based sparse MECAM (DBS-MECAM), are induced by introducing both the decaying  ...  A sparse two-Dimension distance weighted approach for improving the performance of exponential correlation associative memory (ECAM) and modified exponential correlation associative memory (MECAM) is presented  ...  NJUPT "QingLan" Project Foundation and the Returnee's Foundation of China Scholarship Council for partial supports, respectively.  ... 
doi:10.1007/s11063-006-9012-y fatcat:ttp2yupd6jcp3oj4l5z3bzcq7y

Building a mathematical model and an algorithm for training a neural network with sparse dipole synaptic connections for image recognition

Vasyl Lytvyn, Roman Peleshchak, Ivan Peleshchak, Oksana Cherniak, Lyubomyr Demkiv
2021 Eastern-European Journal of Enterprise Technologies  
A training algorithm has been built for a dipole neural network with sparse synaptic connections, which is based on the dipole-dipole interaction between the nearest neurons.  ...  One such neural network that can completely restore a distorted image is a fully connected pseudospin (dipole) neural network that possesses associative memory.  ...  Flowchart of the algorithm for training a sparse dipole neural network  ... 
doi:10.15587/1729-4061.2021.245010 fatcat:khtdfbsrpjd5fntt4qala6rjdq

KANERVA'S SPARSE DISTRIBUTED MEMORY: AN ASSOCIATIVE MEMORY ALGORITHM WELL-SUITED TO THE CONNECTION MACHINE

DAVID ROGERS
1989 International journal of high speed computing  
I discuss the implcmentaticm of the algorithm for s p dis&iiuted memory an the Connedion Machine.  ...  In this paper I descnic the foundations for sparse dislnbutsd memory. and give lome simple examples of using the memory.  ...  ., for their original grant to me when I was working as a postdoctoral fellow at MIT.  ... 
doi:10.1142/s0129053389000196 fatcat:mbcv6y2pynerbefath5bchgzgu

Off the Mainstream: Advances in Neural Networks and Machine Learning for Pattern Recognition

Edmondo Trentin, Friedhelm Schwenker, Neamat El Gayar, Hazem M. Abbas
2018 Neural Processing Letters  
Acknowledgements The present Special Issue is dedicated to the memory of our fellow scientist Ilaria Castelli, PhD, who served as a reviewer for this issue and tragically died at age 34.  ...  We would express our gratitude to the Editors-in-Chief of Neural Processing Letters, who supported our project. We are also thankful to the Springer editorial staff and  ...  pattern recognition by means of neural nets and learning paradigms. The call received an enthusiastic response by the community.  ... 
doi:10.1007/s11063-018-9830-8 fatcat:hxtksb43ujag5nlfrt7atqe7pu

A Theoretical and Experimental Account of n-Tuple Classifier Performance

Richard Rohwer, Michał Morciniec
1996 Neural Computation  
A review of RAM based neural networks. Proceedings of the Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, pp. 58-66. IEEE Computer Society Press, Turin.  ...  Improved memory matrices for the n- tuple pattern recognition method. IEEE Trans. Electron. Comput. 11, 414-415. Bledsoe, W. W., and Browning, I. 1959. Pattern recognition and reading by machine.  ... 
doi:10.1162/neco.1996.8.3.629 fatcat:mmtykf6cjjgbpdg2odg2w3dwhm

Coupled-Oscillator Associative Memory Array Operation for Pattern Recognition

Dmitri E. Nikonov, Gyorgy Csaba, Wolfgang Porod, Tadashi Shibata, Danny Voils, Dan Hammerstrom, Ian A. Young, George I. Bourianoff
2015 IEEE Journal on Exploratory Solid-State Computational Devices and Circuits  
The dependence of errors in association on the number of the memorized patterns and the distance between the test and the memorized pattern is determined for Palm, Furber and Hopfield association algorithms  ...  Operation of the array of coupled oscillators underlying the associative memory function is demonstrated for various interconnection schemes (cross-connect, star phase keying and star frequency keying)  ...  Acknowledgements The authors thank Steven Levitan, Tamas Roska, Matthew Puffal, and William Rippard for fruitful discussions. The authors are grateful for Intel Corporation support of this research.  ... 
doi:10.1109/jxcdc.2015.2504049 fatcat:uqcstbir4bfz7jkbs6f4yto3ay

Coupled-Oscillator Associative Memory Array Operation [article]

Dmitri E. Nikonov, Gyorgy Csaba, Wolfgang Porod, Tadashi Shibata, Danny Voils, Dan Hammerstrom, Ian A. Young, George I. Bourianoff
2013 arXiv   pre-print
The dependence of errors in association on the number of the memorized patterns and the distance between the test and the memorized pattern is determined for Palm, Furber and Hopfield association algorithms  ...  Operation of the array of coupled oscillators underlying the associative memory function is demonstrated for various interconnection schemes (cross-connect, star phase keying and star frequency keying)  ...  Acknowledgements The authors thank Steven Levitan, Tamas Roska, Matthew Puffal, and William Rippard for fruitful discussions. The authors are grateful for Intel Corporation support of this research.  ... 
arXiv:1304.6125v1 fatcat:fg3yzsxsfbg6disgaemnp4l44y

Neural Network Star Pattern Recognition for Spacecraft Attitude Determination and Control

Phillip Alvelda, A. Miguel San Martin
1988 Neural Information Processing Systems  
This paper discusses the latest applications of artificial neural networks to the problem of star pattern recognition for spacecraft attitude determination.  ...  Currently, the most complex spacecraft attitude determination and control tasks are ultimately governed by ground-based systems and personnel.  ...  NEURAL MOTIVATION The parallel architecture and collective computation properties of a neural network based system address several problems associated with the implementation and performance of the serial  ... 
dblp:conf/nips/AlveldaM88 fatcat:cdgt6u2dt5ej3kz6kut3fnof24

Video analytics using beyond CMOS devices

Vijaykrishnan Narayanan, Suman Datta, Gert Cauwenberghs, Don Chiarulli, Steve Levitan, Philip Wong
2014 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2014  
The human vision system understands and interprets complex scenes for a variety of visual tasks in real-time while consuming less than 20 Watts of power.  ...  in the computational fabric and provides an overview of a newgenre of architectures inspired by advances in both the understanding of the visual cortex and the emergence of devices with new mechanisms for  ...  This architecture is amenable to mapping various vision processing algorithms based on spiked neural networks.  ... 
doi:10.7873/date.2014.357 dblp:conf/date/NarayananDCCLW14 fatcat:b2ynwr3qzfdhlbkemnw2jtptra

Guest Editorial Special Issue on Artificial Neural Networks and Statistical Pattern Recognition

A.K. Jain, J. Mao
1997 IEEE Transactions on Neural Networks  
As a result, successful recognition systems based either on statistical approach or neural networks exist only in well-constrained domains (e.g., isolated handprint character recognition and isolated word  ...  Over the past 40 years, a number of different paradigms (statistical, syntactic, neural networks, and fuzzy logic) have been utilized for solving a variety of recognition problems.  ...  The paper by Mazza provides a theoretical analysis of a generic inference rule for recovering stored patterns from noisy data, and capacity of associative memory.  ... 
doi:10.1109/tnn.1997.554186 fatcat:4vroxb2fv5h2fheaogt5o7pbo4

Quantum Hopfield neural network

Patrick Rebentrost, Thomas R. Bromley, Christian Weedbrook, Seth Lloyd
2018 Physical Review A  
Here we employ quantum algorithms for the Hopfield network, which can be used for pattern recognition, reconstruction, and optimization as a realization of a content-addressable memory system.  ...  By introducing a classical technique for operating the Hopfield network, we can leverage quantum algorithms to obtain a quantum computational complexity that is logarithmic in the dimension of the data  ...  Acknowledgments We thank Juan Miguel Arrazola, Mayank Bhatia and Nathan Killoran for fruitful discussions. S. Lloyd was supported by OSD/ARO under the Blue Sky Initiative.  ... 
doi:10.1103/physreva.98.042308 fatcat:7wwt24ffjjhknnfxguzhvkbmta

Detection of Shot Transition in Sports Video Based on Associative Memory Neural Network

Wanli Ke
2022 Wireless Communications and Mobile Computing  
To solve the problem of shot transition detection using a single training sample, an AMNN (Associative Memory Neural Network) model with online learning ability is proposed.  ...  This paper investigates the shot transition detection algorithm in digital video live broadcasts based on sporting events.  ...  Memory is an important part of neural network theory, and it is also an important function of neural networks for intelligent control, pattern recognition, and artificial intelligence.  ... 
doi:10.1155/2022/7862343 doaj:831fb16bfaab42119d593ea657287995 fatcat:3fbwdrarkbc4vnoat36cgfxpry

Exploring and Understanding the High Dimensional and Sparse Image Face Space: a Self-Organized Manifold Mapping [chapter]

Edson C., Emilio M., Gilson A., Carlos E.
2011 New Approaches to Characterization and Recognition of Faces  
This work focuses on the mechanism of pattern completion and the role of the human brain hippocampus as an associative memory to propose a new algorithm for the SOM competitive neural network proposed  ...  The Standard SOM algorithm SOM can be defined as an unsupervised artificial neural network that maps a nonlinear relationship between input patterns in high dimensional space and makes this relationship  ...  The first section presents an architecture for face recognition based on Hidden Markov Models; it is followed by an article on coding methods.  ... 
doi:10.5772/22173 fatcat:vvxooh756zd3lhmiwwrutoyluq

Review of Sparse Distributed Memory

Terry Rooker
1990 The AI Magazine  
His research interests include computer speech recognition using neural networks. Pattern Recognition Scott W.  ...  Such a sparse memory could feasibly be implemented and, depending on the interpretation of the input, used as an associative memory, for best matching, or for any of a variety of other applications.  ...  You are cordially invited to become a member of the AI Community's principal scientific society: The American Association for Artificial Intelligence CONSIDER THESE BENEFITS . . .  ... 
doi:10.1609/aimag.v11i2.837 dblp:journals/aim/Rooker90 fatcat:3gnlppd2xrd2bdsnftvnvlme2e

Spatially Arranged Sparse Recurrent Neural Networks for Energy Efficient Associative Memory

Gouhei Tanaka, Ryosho Nakane, Tomoya Takeuchi, Toshiyuki Yamane, Daiju Nakano, Yasunao Katayama, Akira Hirose
2019 IEEE Transactions on Neural Networks and Learning Systems  
Here, we consider two approaches to finding spatially arranged sparse recurrent neural networks with the high cost-performance ratio for associative memory.  ...  Our results suggest that the presented approaches are useful in seeking more sparse and less costly connectivity of neural networks for the enhancement of energy efficiency in hardware neural networks.  ...  CONCLUSION AND DISCUSSION We have explored sparse recurrent neural networks for associative memory to realize energy efficient information processing in hardware neural networks.  ... 
doi:10.1109/tnnls.2019.2899344 pmid:30892239 fatcat:idjzdwe665aodl7wdpz5jqftx4
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