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Information theory, complexity and neural networks
1989
IEEE Communications Magazine
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For neural networks, measuring the computing performance requires new tools from information theory and computational complexity. ...
For neural networks, measuring the computing performance requires new tools from in formation theory and computational complexity. ...
doi:10.1109/35.41397
fatcat:2toprecxenhmlfapduav4o7zna
Neural Network for Complex Systems: Theory and Applications
2018
Complexity
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Gong et al. studied the pinning synchronous problem for complex networks with interval delays and proposed a series of useful theories. Y. E. ...
Yu presented an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. ...
Acknowledgments The guest editors would like to acknowledge and appreciate the authors and the reviewers for their contribution towards the success of this special issue. ...
doi:10.1155/2018/3141805
fatcat:sow2l3fzhvecjaolirojt4cbjm
Approximation by neural networks and learning theory
2006
Journal of Complexity
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We consider the problem of Learning Neural Networks from samples. The sample size which is sufficient for obtaining the almost-optimal stochastic approximation of function classes is obtained. ...
Let m ∈ N and ε > 0 be any number. ...
Acknowledgements The author is grateful to Allan Pinkus, Sergeii Konyagin, Vladimir Temlyakov and Joel Ratsaby for helpful discussions and remarks. ...
doi:10.1016/j.jco.2005.09.001
fatcat:7tatjhjwe5fyzbts4gb4hjqc64
COMPLEX DATA CLUSTERING: FROM NEURAL NETWORK ARCHITECTURE TO THEORY AND APPLICATIONS OF NONLINEAR DYNAMICS OF PATTERN RECOGNITION
2014
BIOMAT 2013
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We also present some applications to gene filtering, cancer diagnosis, neural spike trains pattern recognition, text mining, stock associations, and online social network news aggregation. ...
Our objective is to develop both mathematical foundation and effective techniques/tools for projective clustering. ...
Consequently, we hope further development, in terms of neural physiological evidence, the qualitative and numerical theory, and applications, of this neural network architecture should provide inspiration ...
doi:10.1142/9789814602228_0005
fatcat:okqenmoisbh35clr7kjsc36mla
Neural-based routing strategy with transmission information for complex communication networks
2015
Nonlinear Theory and Its Applications IEICE
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In the mutually connected neural network, the ilth neuron corresponds to the connection between the ith and lth nodes. ...
We now evaluate the effectiveness of the routing strategy based on the mutually connected neural networks [9] for regular and irregular topological communication networks (see Figs. 2 and 3). ...
doi:10.1587/nolta.6.263
fatcat:xmqsywa3jrh5dirr24jydh3mfq
Complex dynamic phenomena in space-invariant cellular neural networks
2002
IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications
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It is shown that first-order autonomous space-invariant cellular neural networks (CNNs) may exhibit a complex dynamic behavior (i.e., equilibrium point and limit cycle bifurcation, strange and chaotic ...
It is worth noting that most practical CNN implementations exploit first-order cells and space-invariant templates: so far no example of complex dynamics has been shown in first-order autonomous space-invariant ...
Chua and Prof. T. Roska for their scientific interest in this activity and to Prof. G. ...
doi:10.1109/81.989168
fatcat:alieuyc3ubhyvi6zqvmgnjbtaa
Synchrony and Complexity in State-Related EEG Networks: An Application of Spectral Graph Theory
2020
Neural Computation
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Furthermore, we found that complexity in the investigated brain networks is inversely related to the stability of synchronizability. ...
To answer these questions, we tested the application of the spectral graph theory and the Shannon entropy as alternative approaches in neuroimaging. ...
However, although these measures can demonstrate the complexity of synchronization and desynchronization between two neural regions or electrodes, they cannot evaluate the complexity of global brain networks ...
doi:10.1162/neco_a_01327
pmid:32946707
fatcat:szrlgjbln5fwhjfdxzfmer55xa
Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm
2017
Complexity
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and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. ...
According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole. ...
Data Calibration Algorithm Based on RBF Neural Network. ...
doi:10.1155/2017/5067145
fatcat:dmbkgyrojzdnlb32i6c7q4gmpm
Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks
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properties of both the prior distribution over concepts and the sequence of instances seen by the learner, and to smoothly unite in a common framework the popular statistical physics and VC dimension theories ...
To achieve this, we undertake a systematic investigation and comparison of two fundamental quantities in learning and information theory: the probability of an incorrect prediction for an optimal learning ...
Acknowledgements We are greatly indebted to Manfred Opper and Ron Rivest for their valuable suggestions and guidance, and to Sara Solla and Naftali Tishby for insightful ideas in the early stages of this ...
doi:10.1109/ijcnn.1992.226964
fatcat:lqmlccduqza65hskiyfben5hrq
A dynamic mechanism of Alzheimer based on artificial neural network
[article]
2014
arXiv
pre-print
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We analyze the dynamic mechanism of Alzheimer Disease based on the cognitive model that established from artificial neural network. ...
The computational complexity of neural network From the views of artificial neural network theory, we can find why the cognitive depth decreasing is due to the decreasing of neural network computational ...
Conclusion In this paper, we analyze the neural network's computational complexity of human brain by use the artificial neural network theories. ...
arXiv:1411.4221v1
fatcat:vr2z7qrzwvbc7lmeqjvjay6hsy
Biological Lattice Gauge Theory as Modeling of Quantum Neural Networks
2018
Journal of Modeling and Optimization
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Based on quantum biology and biological gauge field theory, we propose the biological lattice gauge theory as modeling of quantum neural networks. ...
Further, we discuss the model of Neural Networks (NN) and the quantum neutral networks, which are related with biological loop quantum theory. ...
For example, the neural elements in neural networks correspond to nodes, and fields related complexity. Nodes and fields may form different levels and hierarchical networks. ...
doi:10.32732/jmo.2018.10.1.23
fatcat:hi4sixjsnbbuxpirpad4lyylba
Consilience of Reductionism and Complexity Theory in Language Research: Adaptive Weight Model
2022
Complexity
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This paper starts by discussing the adaptability of complex dynamic systems and combines cognitive processing model and artificial neural networks to construct and verify an adaptive weight model, showing ...
Reductionism and complexity theory are two paradigms frequently found in language research. ...
Acknowledgments is work was supported in part by the National Social Science Foundation of China (grant nos. 19BYY217 and 20BYY212) and in part by the National Social Science Foundation of Education of ...
doi:10.1155/2022/4216206
fatcat:h5m4selehnhhvhnlhpxztue7qe
Errata to "learning efficiency of redundant neural networks in bayesian estimation"
2002
IEEE Transactions on Neural Networks
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Wah, Artificial Neural Networks: Concepts and
Theory: IEEE Comput. Society Press, 1992.
[3] Y. Zhao, "On-line neural network learning algorithm with exponential
convergence rate," Electron. ...
Zhou and J. Si, "Advanced neural network training algorithm with
reduced complexity based on Jacobian deficiency," IEEE Trans. Neural
Networks, vol. 9, pp. 448-453, May 1998.
[5] R. ...
His research interests include probability theory, mathematical statistics, and neural-network learning theory. Dr. Watanabe is a member of IEICE and the Japanese Neural Network Society. ...
doi:10.1109/tnn.2002.977325
pmid:18244428
fatcat:d7cg2g2ej5h7vfeferowl2ykru
Classification by Ensembles of Neural Networks
[article]
2012
arXiv
pre-print
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This approach differs from the standard one based on the optimization theory. In particular, any neural network from the mentioned ensemble may not be an approximation of the objective function. ...
We introduce a new procedure for training of artificial neural networks by using the approximation of an objective function by arithmetic mean of an ensemble of selected randomly generated neural networks ...
Acknowledgments The author gratefully acknowledges being partially supported by the grants of the Russian Foundation for Basic Research RFBR 11-01-00828-a and 11-01-12114-ofi- ...
arXiv:1202.4170v1
fatcat:hzkwvx7p4ffxdhhmn73wux2tfi
Page 4279 of Mathematical Reviews Vol. , Issue 95g
[page]
1995
Mathematical Reviews
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Taylor and Mark D. Plumbley, Information theory and neural networks (307-340); J. G. Taylor and F. N. Alavi, Mathematical analysis of a competitive network for attention (341-382). ...
Another paper of interest is one by the editor, Taylor (with Plumbley), entitled “Information theory and neural networks”. ...
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