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Information theory, complexity and neural networks

Y.S. Abu-Mostafa
1989 IEEE Communications Magazine  
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

Chenguang Yang, Jing Na, Guang Li, Yanan Li, Junpei Zhong
2018 Complexity  
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

V. Maiorov
2006 Journal of Complexity  
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

GUOJUN GAN, JIALUN YIN, YULIA WANG, JIANHONG WU
2014 BIOMAT 2013  
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

Takayuki Kimura, Tohru Takamizawa, Keisuke Kimura, Kenya Jin'no
2015 Nonlinear Theory and Its Applications IEICE  
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

M. Biey, M. Gilli, P. Checco
2002 IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications  
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

Amir Hossein Ghaderi, Bianca R Baltaretu, Masood Nemati Andevari, Vishal Bharmauria, Fuat Balci
2020 Neural Computation  
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

Li Wang, Shimin Lin, Jingfeng Yang, Nanfeng Zhang, Ji Yang, Yong Li, Handong Zhou, Feng Yang, Zhifu Li
2017 Complexity  
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

M. Kearns
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks  
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]

Zhi Cheng
2014 arXiv   pre-print
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

Yi-Fang Chang
2018 Journal of Modeling and Optimization  
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

Chao Zhang, Rosa M. Benito
2022 Complexity  
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"

S. Watanabe
2002 IEEE Transactions on Neural Networks  
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]

S.V. Kozyrev
2012 arXiv   pre-print
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  
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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