Filters








338 Hits in 4.1 sec

An Improved Transiently Chaotic Neural Network with Multiple Chaotic Dynamics for Maximum Clique Problem

Gang Yang, Junyan Yi, Shangce Gao, Zheng Tang
2007 Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007)  
We propose an improved chaotic maximum neural network to solve maximum clique problem.  ...  Based on the analysis, a random nonlinear self-feedback and flexible annealing strategy are embedded in maximum neural network, which makes the network more powerful to escape local minima and be independent  ...  A random nonlinear self-feedback is embedded in the maximum neural network, which creates more efficient chaotic dynamics to the network.  ... 
doi:10.1109/icicic.2007.152 fatcat:ikmppdzng5eeniq2tsksqkhmga

An improved competitive Hopfield network with inhibitive competitive activation mechanism for maximum clique problem

Gang Yang, Nan Yang, Junyan Yi, Zheng Tang
2014 Neurocomputing  
In this paper, we analyze the formula of weights definition in the discrete competitive Hopfield network (DCHOM) and point out its flaw when using it to solve some special instances of maximum clique problem  ...  Our algorithm effectively overcomes the flaw of the DCHOM, and exhibits powerful solving ability for the MCP.  ...  In the ICHN, the ICAM provides a nonlinear self-feedback correlative with the competitive inputting variants u xi and u xj but not v xi and v xj like other self-feedback networks normally.  ... 
doi:10.1016/j.neucom.2012.11.055 fatcat:xlfmrizukrbgdcoazdpmjb3eca

Page 2938 of Mathematical Reviews Vol. , Issue 2002D [page]

2002 Mathematical Reviews  
Summary: “Through adding a nonlinear self-feedback term to the evolution equations of neural network, we introduce a transiently chaotic neural network model.  ...  Only a few heuris- tics have been developed with the goal of finding tighter upper bounds for the maximum-weighted clique problem.  ... 

Table of Contents

2022 IEEE Transactions on Cybernetics  
Guan 700 Event-Triggered Adaptive Fuzzy Output-Feedback Control for Nonstrict-Feedback Nonlinear Systems With Asymmetric Output Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Matsuno 312 A Novel Self-Organizing Fuzzy Neural Network to Learn and Mimic Habitual Sequential Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tcyb.2021.3138650 fatcat:zwcf4txyx5dafbdtm6rayoeoae

Learning from Data to Optimize Control in Precision Farming

Alexander Kocian, Luca Incrocci
2020 Stats  
This special issue presents the latest development in statistical inference, machine learning, and optimum control for precision farming.  ...  Precision farming is one way of many to meet a 55 percent increase in global demand for agricultural products on current agricultural land by 2050 at reduced need of fertilizers and efficient use of water  ...  Bayesian Neural Networks model the uncertainty of the estimated edge weights by interpreting them as maximum likelihood or maximum a posteriori estimates.  ... 
doi:10.3390/stats3030018 fatcat:5kmzlbrzfjcsph63kjghlux4je

Cognitive radio spectrum allocation using genetic algorithm

Jamal Elhachmi, Zouhair Guennoun
2016 EURASIP Journal on Wireless Communications and Networking  
This paper presents the problem formulation, development, and use of a robust dynamic genetic algorithm (GA) for channel allocation in cognitive radio.  ...  Compared with existing methods, simulation results demonstrate that our approach algorithm produces satisfactory results with reduced network interference and enhance efficiently the spectrum throughput  ...  We now show that the maximum clique problem can be reduced to the DListColor problem in polynomial time and that the maximum clique problem has a solution if and only if DListColor has a solution.  ... 
doi:10.1186/s13638-016-0620-6 fatcat:z3ojevx6xrc6tgxhg4w4pocbgu

2014 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 25

2014 IEEE Transactions on Neural Networks and Learning Systems  
., +, TNNLS Feb. 2014 278-288 Search problems A Minimum Resource Neural Network Framework for Solving Multiconstraint Shortest Path Problems.  ...  ., +, TNNLS Aug. 2014 1566-1582 Self-adjusting systems Cooperative Tracking Control of Nonlinear Multiagent Systems Using Self-Structuring Neural Networks.  ...  The Field of Values of a Matrix and Neural Networks. Georgiou, G.M., TNNLS Sep. 2014  ... 
doi:10.1109/tnnls.2015.2396731 fatcat:ztnfcozrejhhfdwg7t2f5xlype

Learning from Data to Optimize Control in Precision Farming [article]

Alexander Kocian, Luca Incrocci
2020 arXiv   pre-print
This special issue presents the latest development in statistical inference, machine learning and optimum control for precision farming.  ...  Precision farming is one way of many to meet a 70 percent increase in global demand for agricultural products on current agricultural land by 2050 at reduced need of fertilizers and efficient use of water  ...  Bayesian Neural Networks model the uncertainty of the estimated edge weights by interpreting them as maximum likelihood or maximum a posteriori estimates.  ... 
arXiv:2007.05493v1 fatcat:fgjr6kabqba5bl4a76wb4krtpi

Exact-K Recommendation via Maximal Clique Optimization [article]

Yu Gong, Yu Zhu, Lu Duan, Qingwen Liu, Ziyu Guan, Fei Sun, Wenwu Ou, Kenny Q. Zhu
2019 arXiv   pre-print
To tackle this specific combinatorial optimization problem which is NP-hard, we propose Graph Attention Networks (GAttN) with a Multi-head Self-attention encoder and a decoder with attention mechanism.  ...  Thus we take the first step to give a formal problem definition, and innovatively reduce it to Maximum Clique Optimization based on graph.  ...  Here we use a simple fully connected neural network with nonlinear activation ReLU as: x i = Re LU (W I [x s i ; x u ] + b I ), (4) where x s i and x u are feature vectors for item s i and user u (e.g  ... 
arXiv:1905.07089v1 fatcat:llt5kzvxqngsto2lyugbrqr7ii

A Gradual Noisy Chaotic Neural Network for Solving the Broadcast Scheduling Problem in Packet Radio Networks

L. Wang, H. Shi
2006 IEEE Transactions on Neural Networks  
Index Terms-Broadcast scheduling problem, gradual noisy chaotic neural network, NP-complete, packet radio network.  ...  In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks.  ...  ACKNOWLEDGMENT The authors would like to sincerely thank the Associate Editor and the reviewers for their constructive comments and suggestions that helped to improve the manuscript significantly.  ... 
doi:10.1109/tnn.2006.875976 pmid:16856661 fatcat:kazuigsi5vbgznv2zz7ttdoyva

Reactive Search Optimization: Learning While Optimizing [chapter]

Roberto Battiti, Mauro Brunato
2010 International Series in Operations Research and Management Science  
Reactive Search Optimization has to do with learning for optimizing, with the insertion of a machine learning component into a solution process so that algorithm selection, adaptation, integration, are  ...  Needless to say, studying and designing satisfactory solutions to the above final goal is a long-term enterprise with opportunities for PhD students and researchers of this century, but we feel that the  ...  An RSO scheme is applied to the maximum clique problem in graphs in [19] [23] .  ... 
doi:10.1007/978-1-4419-1665-5_18 fatcat:vimnftpcvrfw7cresqaddwhp64

Grasp: An Annotated Bibliography [chapter]

Paola Festa, Mauricio G.C. Resende
2002 Operations Research/Computer Science Interfaces Series  
A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization.  ...  GRASP has been applied to a wide range of combinatorial optimization problems, ranging from scheduling and routing to drawing and turbine balancing.  ...  An exact parallel algorithm for the maximum clique problem. In R. De Leone et al., editor, High performance algorithms and software in nonlinear optimization, pages 279-300.  ... 
doi:10.1007/978-1-4615-1507-4_15 fatcat:kvaokik4m5a2bezgzzcfaqjbui

Hopfield type of Artificial Neural Network via Election Algorithm as Heuristic Search method for Random Boolean kSatisfiability

Hamza Abubakar, Mohammed Lawal Danrimi
2021 International Journal of Computing and Digital Systems  
The main objective is to improve the learning phase of Hopfield type artificial neural network (HNN) for optimal Random Boolean kSatisfiability representation for higher-order logic.  ...  patterns and predict information which provides a powerful mechanism for optimizations/search problems and other decision-making problem.  ...  The Hopfield network is a cyclic neural network with feedback connections from output to input.  ... 
doi:10.12785/ijcds/100163 fatcat:bzhxv3n2vrdtvjw265geub2iqa

Iterative free-energy optimization for recurrent neural networks (INFERNO)

Alexandre Pitti, Philippe Gaussier, Mathias Quoy, Wael El-Deredy
2017 PLoS ONE  
such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.  ...  As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal  ...  Acknowledgments We thank Souheil Hanoune and Karl Friston as well as the reviewers for interesting comments and fruitful feedback on anterior versions of the paper.  ... 
doi:10.1371/journal.pone.0173684 pmid:28282439 pmcid:PMC5345841 fatcat:ltk2vtdwprgc3hdki4kwkdmx24

Cancer: A turbulence problem

Abicumaran Uthamacumaran
2020 Neoplasia: An International Journal for Oncology Research  
Finding the clique or master GRNs (Gene Regulatory Networks) controlling CSC fate transitions is a complex decision problem [15 , 16] .  ...  A Galilean-invariance embedded, DLN network architecture (Tensor Basis Neural Network) underwent training on various turbulent flow datasets followed by the Bayesian optimization for the neural network's  ... 
doi:10.1016/j.neo.2020.09.008 pmid:33142240 pmcid:PMC7588841 fatcat:migyi4rxqndmxgqhyxsiyqpatu
« Previous Showing results 1 — 15 out of 338 results