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Improving the performance of the Hopfield-Tank neural network through normalization and annealing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf00339943openalex-snapshot
“Neural” computation of decisions in optimization problems
inferredcites →https://doi.org/10.1007/bf00363956openalex-snapshot
On the stability of the Travelling Salesman Problem algorithm of Hopfield and Tank
inferredcites →https://doi.org/10.1016/0893-6080(88)90183-9openalex-snapshot
A neural network for the optimization of communications network design
inferredcites →https://doi.org/10.1038/326689a0openalex-snapshot
An analogue approach to the travelling salesman problem using an elastic net method
inferredcites →https://doi.org/10.1109/31.31321openalex-snapshot
A digital architecture employing stochasticism for the simulation of Hopfield neural nets
inferredcites →https://doi.org/10.1109/icnn.1988.23830openalex-snapshot
An improved elastic net method for the traveling salesman problem
inferredcites →https://doi.org/10.1109/icnn.1988.23832openalex-snapshot
Solution of the traveling salesman problem with an adaptive ring
inferredcites →https://doi.org/10.1109/icnn.1988.23937openalex-snapshot
Fast TSP algorithm based on binary neuron output and analog neuron input using the zero-diagonal interconnect matrix and necessary and sufficient constraints of the permutation matrix
inferredcites →https://doi.org/10.1109/icnn.1988.23941openalex-snapshot
Determination of parameters in a Hopfield/Tank computational network
inferredcites →https://doi.org/10.1109/icnn.1988.23942openalex-snapshot
A traveling salesman objective function that works
inferredcites →https://doi.org/10.1109/icnn.1988.23945openalex-snapshot
Alternative networks for solving the traveling salesman problem and the list-matching problem
inferredcites →https://doi.org/10.1126/science.220.4598.671openalex-snapshot
Optimization by Simulated Annealing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/(sici)1096-9128(199903)11:3<155::aid-cpe424>3.0.co;2-xopenalex-snapshot
Simulated annealing task to processor mapping for domain decomposition methods on distributed parallel computers
inferred← citeshttps://doi.org/10.1002/(sici)1097-0207(19990220)44:5<593::aid-nme516>3.0.co;2-0openalex-snapshot
A neural network graph partitioning procedure for grid-based domain decomposition
inferred← citeshttps://doi.org/10.1007/0-306-48056-5_15openalex-snapshot
Artificial Neural Networks for Combinatorial Optimization
inferred← citeshttps://doi.org/10.1007/0-387-23550-7_10openalex-snapshot
Neural Networks for Residential Infrastructure Management
inferred← citeshttps://doi.org/10.1007/3-540-28847-3_8openalex-snapshot
Neural Networks without Training for Optimization
inferred← citeshttps://doi.org/10.1007/3-540-44868-3_50openalex-snapshot
Performance-Enhancing Bifurcations in a Self-organising Neural Network
inferred← citeshttps://doi.org/10.1007/3-540-44868-3_51openalex-snapshot
A Competitive Neural Network based on dipoles
inferred← citeshttps://doi.org/10.1007/3-540-45720-8_88openalex-snapshot
Nonlinear System Dynamics in the Normalisation Process of a Self-Organising Neural Network for Combinatorial Optimisation
inferred← citeshttps://doi.org/10.1007/3-540-58430-7_71openalex-snapshot
An efficient mapping heuristic for mesh-connected parallel architectures based on Mean Field Annealing
inferred← citeshttps://doi.org/10.1007/3-540-59119-2_186openalex-snapshot
Some theorems concerning the free energy of (Un) constrained stochastic Hopfield neural networks
inferred← citeshttps://doi.org/10.1007/3-540-60590-8_15openalex-snapshot
A free energy minimization framework for inference problems in modulo 2 arithmetic
inferred← citeshttps://doi.org/10.1007/3-540-62909-2_83openalex-snapshot
Self annealing: Unifying deterministic annealing and relaxation labeling
inferred← citeshttps://doi.org/10.1007/978-1-4419-7997-1_43openalex-snapshot
Dynamical System Approaches to Combinatorial Optimization∗
inferred← citeshttps://doi.org/10.1007/978-1-4419-7997-1_65openalex-snapshot
Neural Network Models in Combinatorial Optimization
inferred← citeshttps://doi.org/10.1007/978-1-4471-0509-1_15openalex-snapshot
Facility Location Using Neural Networks
inferred← citeshttps://doi.org/10.1007/978-1-4471-2063-6_282openalex-snapshot
Neural Networks and the Travelling Salesman Problem
inferred← citeshttps://doi.org/10.1007/978-1-4613-0303-9_18openalex-snapshot
Dynamical System Approaches to Combinatorial Optimization
inferred← citeshttps://doi.org/10.1007/978-1-4757-4137-7_8openalex-snapshot
A Potts Neural Network Heuristic for the Class/Teacher Timetabling Problem
inferred← citeshttps://doi.org/10.1007/978-3-319-11179-7_39openalex-snapshot
Structure Perturbation Optimization for Hopfield-Type Neural Networks
inferred← citeshttps://doi.org/10.1007/978-3-540-28647-9_67openalex-snapshot
A Method to Improve the Transiently Chaotic Neural Network
inferred← citeshttps://doi.org/10.1007/978-3-7091-6230-9_14openalex-snapshot
Graded Rescaling in Hopfield Networks
inferred← citeshttps://doi.org/10.1007/978-3-7091-6384-9_12openalex-snapshot
Improving the Performance of the Hopfield Network By Using A Relaxation Rate
inferred← citeshttps://doi.org/10.1007/978-3-7091-6384-9_13openalex-snapshot
A New Activation Function in the Hopfield Network for Solving Optimization Problems
inferred← citeshttps://doi.org/10.1007/bf00202388openalex-snapshot
Suboptimum solutions obtained by the Hopfield-Tank neural network algorithm
inferred← citeshttps://doi.org/10.1007/bf00929928openalex-snapshot
Scalable VLSI implementations for neural networks
inferred← citeshttps://doi.org/10.1007/s00422-024-01002-0openalex-snapshot
Beyond the Nobel prizes: towards new synergies between Computational Neuroscience and Artificial Intelligence
inferred← citeshttps://doi.org/10.1007/s004220050520openalex-snapshot
K-way neural network graph partitioning with separator vertices
inferred← citeshttps://doi.org/10.1016/0305-0548(92)90044-6openalex-snapshot
Neural network methods in combinatorial optimization
inferred← citeshttps://doi.org/10.1016/0377-2217(96)00042-2openalex-snapshot
Comparison of neural and heuristic methods for a timetabling problem
inferred← citeshttps://doi.org/10.1016/0743-7315(92)90013-dopenalex-snapshot
A new mapping heuristic based on mean field annealing
inferred← citeshttps://doi.org/10.1016/0925-2312(94)00016-lopenalex-snapshot
Circuit partitioning using mean field annealing
inferred← citeshttps://doi.org/10.1016/b978-0-12-741252-8.50011-xopenalex-snapshot
Hopfield Model and Optimization Problems
inferred← citeshttps://doi.org/10.1016/b978-0-444-88400-8.50030-3openalex-snapshot
Rapid Prototyping for Neural Networks
inferred← citeshttps://doi.org/10.1016/b978-0-444-88400-8.50051-0openalex-snapshot
REFERENCES FROM ALL CONTRIBUTIONS
inferred← citeshttps://doi.org/10.1016/b978-0-444-89178-5.50009-9openalex-snapshot
PREDICTING THE ANNEALING RANGE BY COMPUTING CRITICAL TEMPERATURE IN MEAN FIELD ANNEALING FOR THE TRAVELING SALESMAN PROBLEM
inferred← citeshttps://doi.org/10.1016/j.asoc.2018.06.003openalex-snapshot
Bio-inspired neurocomputing with 256 noise oscillators simulating photo response of Euglena cells
inferred← citeshttps://doi.org/10.1016/j.bbe.2018.01.002openalex-snapshot
An epileptic seizure detection system based on cepstral analysis and generalized regression neural network
inferred← citeshttps://doi.org/10.1016/j.neucom.2004.12.004openalex-snapshot
A method to improve the transiently chaotic neural network
inferred← citeshttps://doi.org/10.1016/j.neucom.2005.03.003openalex-snapshot
Lagrangian object relaxation neural network for combinatorial optimization problems
inferred← citeshttps://doi.org/10.1016/j.neunet.2007.12.007openalex-snapshot
How language can help discrimination in the Neural Modelling Fields framework
inferred← citeshttps://doi.org/10.1016/j.tig.2026.01.013openalex-snapshot
Machine learning for evolutionary genetics and molecular evolution
inferred← citeshttps://doi.org/10.1016/s0031-3203(99)00077-1openalex-snapshot
Self-annealing and self-annihilation: unifying deterministic annealing and relaxation labeling
inferred← citeshttps://doi.org/10.1016/s0129-0657(02)00105-9openalex-snapshot
A SEMI-LOCALIZED ELASTIC NET FOR SURFACE RECONSTRUCTION OF OBJECTS FROM MULTISLICE IMAGES
inferred← citeshttps://doi.org/10.1016/s0305-0548(99)00141-0openalex-snapshot
Neural networks in business: techniques and applications for the operations researcher
inferred← citeshttps://doi.org/10.1016/s0360-8352(02)00180-8openalex-snapshot
Hopfield neural networks for timetabling: formulations, methods, and comparative results
inferred← citeshttps://doi.org/10.1016/s0377-2217(03)00099-7openalex-snapshot
A comparison of discrete and continuous neural network approaches to solve the class/teacher timetabling problem
inferred← citeshttps://doi.org/10.1016/s0736-5845(01)00015-1openalex-snapshot
Machine grouping using sequence-based similarity coefficients and neural networks
inferred← citeshttps://doi.org/10.1016/s0893-6080(05)80074-7openalex-snapshot
An analytical framework for optimizing neural networks
inferred← citeshttps://doi.org/10.1016/s0893-6080(96)00106-2openalex-snapshot
Chaotic Potts Spin Model for Combinatorial Optimization Problems
inferred← citeshttps://doi.org/10.1016/s0893-6080(98)00077-xopenalex-snapshot
Constrained neural approaches to quadratic assignment problems
inferred← citeshttps://doi.org/10.1016/s0893-6080(98)00089-6openalex-snapshot
A fast neural-network algorithm for VLSI cell placement
inferred← citeshttps://doi.org/10.1016/s0925-2312(01)00343-5openalex-snapshot
Doubly constrained network for combinatorial optimization
inferred← citeshttps://doi.org/10.1016/s0959-440x(96)80078-9openalex-snapshot
Mean-field minimization methods for biological macromolecules
inferred← citeshttps://doi.org/10.1016/s0965-9978(99)00058-7openalex-snapshot
Analysis of a deformed three-dimensional culvert structure using neural networks
inferred← citeshttps://doi.org/10.1017/s0890060400003103openalex-snapshot
Potential of artificial neural networks for resource scheduling
inferred← citeshttps://doi.org/10.1023/a:1013324605452openalex-snapshot
Evolutionary Algorithms for Allocating Data in Distributed Database Systems
inferred← citeshttps://doi.org/10.1023/a:1018780424422openalex-snapshot
A New Relaxation Procedure in the Hopfield Network for Solving Optimization Problems
inferred← citeshttps://doi.org/10.1023/a:1021849410328openalex-snapshot
Tabu Machine: A New Neural Network Solution Approach for Combinatorial Optimization Problems
inferred← citeshttps://doi.org/10.1038/s41534-022-00623-2openalex-snapshot
Exploiting degeneracy in belief propagation decoding of quantum codes
inferred← citeshttps://doi.org/10.1049/el:19950331openalex-snapshot
Free energy minimisation algorithmfor decoding and cryptanalysis
inferred← citeshttps://doi.org/10.1049/ip-gtd:19970866openalex-snapshot
Large-scale economic dispatch using an improved Hopfield neural network
inferred← citeshttps://doi.org/10.1049/ip-gtd:20040052openalex-snapshot
Hybrid algorithm for optimisation of m-loop electric power distribution networks
inferred← citeshttps://doi.org/10.1049/ip-rsn:19960249openalex-snapshot
Performance of neural data associator
inferred← citeshttps://doi.org/10.1063/1.476402openalex-snapshot
Building protein lattice models using self-consistent mean field theory
inferred← citeshttps://doi.org/10.1080/07408179508936794openalex-snapshot
A neural algorithm for finding the shortest flow path for an automated guided vehicle system
inferred← citeshttps://doi.org/10.1088/2058-9565/ad7b6fopenalex-snapshot
Practical twin-field quantum key distribution parameter optimization based on quantum annealing algorithm
inferred← citeshttps://doi.org/10.1093/ietcom/e91-b.1.110openalex-snapshot
Autonomous and Decentralized Optimization of Large-Scale Heterogeneous Wireless Networks by Neural Network Dynamics
inferred← citeshttps://doi.org/10.1101/260877openalex-snapshot
Phylogeny Recapitulates Learning: Self-Optimization of Genetic Code
inferred← citeshttps://doi.org/10.1103/physreve.53.5153openalex-snapshot
Bifurcations in mean-field-theory annealing
inferred← citeshttps://doi.org/10.1109/21.281427openalex-snapshot
Finding knight's tours on an M×N chessboard with O(MN) hysteresis McCulloch-Pitts neurons
inferred← citeshttps://doi.org/10.1109/34.297953openalex-snapshot
A neural network approach to CSG-based 3-D object recognition
inferred← citeshttps://doi.org/10.1109/3477.775265openalex-snapshot
Scheduling multiprocessor job with resource and timing constraints using neural networks
inferred← citeshttps://doi.org/10.1109/49.62825openalex-snapshot
Optimization of transmitter and receiver filters for digital communication systems using mean field annealing
inferred← citeshttps://doi.org/10.1109/5.537110openalex-snapshot
Neural network optimization for multi-target multi-sensor passive tracking
inferred← citeshttps://doi.org/10.1109/72.501726openalex-snapshot
Generalized deterministic annealing
inferred← citeshttps://doi.org/10.1109/72.548165openalex-snapshot
A Lagrangian relaxation network for graph matching.
inferred← citeshttps://doi.org/10.1109/72.548187openalex-snapshot
An argument for abandoning the travelling salesman problem as a neural-network benchmark
inferred← citeshttps://doi.org/10.1109/72.80231openalex-snapshot
Graph partitioning using annealed neural networks
inferred← citeshttps://doi.org/10.1109/bmei.2014.7002909openalex-snapshot
An intelligent algorithm based on neural network for combinatorial optimization problems
inferred← citeshttps://doi.org/10.1109/ccece.1993.332291openalex-snapshot
Fast contingency screening through optimizing Hopfield neural networks
inferred← citeshttps://doi.org/10.1109/cvpr.1991.139797openalex-snapshot
A neural network approach to CSG-based 3-D object recognition
inferred← citeshttps://doi.org/10.1109/cvpr.2005.544openalex-snapshot
Tracking Cell Signals in Fluorescent Images
inferred← citeshttps://doi.org/10.1109/icc.1995.524472openalex-snapshot
A neural network shortest path algorithm for routing in packet-switched communication networks
inferred← citeshttps://doi.org/10.1109/icics.1997.652066openalex-snapshot
A generalized model of statistical Hopfield neural network to solve TSP
inferred← citeshttps://doi.org/10.1109/icisce.2016.33openalex-snapshot
A Stochastic Neuron Model for Finding a Near-Maximum Independent Set of a Circle Graph
inferred← citeshttps://doi.org/10.1109/icnn.1993.298566openalex-snapshot
Annealing networks and fractal landscapes
inferred← citeshttps://doi.org/10.1109/icnn.1993.298569openalex-snapshot
Solving the scheduling problem in high level synthesis using a normalized mean field neural network
inferred← citeshttps://doi.org/10.1109/icnn.1993.298728openalex-snapshot
An unsupervised learning rule for vector normalization and gain control
inferred← citeshttps://doi.org/10.1109/icnn.1994.375019openalex-snapshot
A hierarchical approach for solving large-scale traveling salesman problem
inferred← citeshttps://doi.org/10.1109/icnn.1994.375022openalex-snapshot
A Lagrangian relaxation network for graph matching
inferred← citeshttps://doi.org/10.1109/icnn.1995.487829openalex-snapshot
Using the Hopfield neural network with mean field annealing to solve the shortest path problem in a communication network
inferred← citeshttps://doi.org/10.1109/icnn.1995.488904openalex-snapshot
Higher order neural networks for combinatorial optimisation improving the scaling properties of the Hopfield network
inferred← citeshttps://doi.org/10.1109/iconip.2002.1202801openalex-snapshot
Characteristic updating-normalisation dynamics of a self-organising neural network for enhanced combinatorial optimisation
inferred← citeshttps://doi.org/10.1109/icsmc.1995.538235openalex-snapshot
Using the Hopfield model with mean field annealing to solve the routing problem in packet-switched communication networks
inferred← citeshttps://doi.org/10.1109/ijcnn.1990.137633openalex-snapshot
The VLSI implementation of STONN
inferred← citeshttps://doi.org/10.1109/ijcnn.1990.137931openalex-snapshot
The definition of necessary hidden units in neural networks for combinatorial optimization
inferred← citeshttps://doi.org/10.1109/ijcnn.1990.137943openalex-snapshot
Encoding logical constraints into neural network cost functions
inferred← citeshttps://doi.org/10.1109/ijcnn.1992.227259openalex-snapshot
Dynamical stability and parameter selection in neural optimization
inferred← citeshttps://doi.org/10.1109/ijcnn.1992.227298openalex-snapshot
An estimation of parameters in an energy function used in a simulated annealing method
inferred← citeshttps://doi.org/10.1109/ijcnn.1993.716858openalex-snapshot
Machine and bit precision influence on the Hopfield-Tank model for the TSP
inferred← citeshttps://doi.org/10.1109/ijcnn.1999.831534openalex-snapshot
Extending the power and capacity of constraint satisfaction networks
inferred← citeshttps://doi.org/10.1109/ijcnn.2000.859444openalex-snapshot
Rescaling the energy function in Hopfield networks
inferred← citeshttps://doi.org/10.1109/ijcnn.2001.939525openalex-snapshot
Improving the Hopfield network through beam search
inferred← citeshttps://doi.org/10.1109/ijcnn.2002.1005596openalex-snapshot
Optimization by varied beam search in Hopfield networks
inferred← citeshttps://doi.org/10.1109/ijcnn.2006.1716786openalex-snapshot
Categorization and symbol grounding in a complex environment
inferred← citeshttps://doi.org/10.1109/ijcnn.2007.4371125openalex-snapshot
Language acquisition and category discrimination in the Modeling Field Theory framework
inferred← citeshttps://doi.org/10.1109/iscas.1993.394158openalex-snapshot
Quadrisectioning based placement with a normalized mean field neural network
inferred← citeshttps://doi.org/10.1109/iscas.1993.693082openalex-snapshot
Quadrisectioning based placement with a normalized mean field neural network
inferred← citeshttps://doi.org/10.1109/iscas.1994.409198openalex-snapshot
A distributed reconfiguration controller for linear array harvest problem: hierarchically quasi-normalized neural approach
inferred← citeshttps://doi.org/10.1109/pimrc.2007.4394658openalex-snapshot
Distributed Optimization based on Neurodynamics for Cognitive Wireless Clouds
inferred← citeshttps://doi.org/10.1109/pimrc.2008.4699900openalex-snapshot
Decentralized optimization of wireless sensor network lifetime based on neural network dynamics
inferred← citeshttps://doi.org/10.1109/spdp.1991.218253openalex-snapshot
Circuit partitioning using parallel mean field annealing algorithms
inferred← citeshttps://doi.org/10.1109/tai.1998.744856openalex-snapshot
Multiconstraint task scheduling in multi-processor system by neural network
inferred← citeshttps://doi.org/10.1109/tcsi.2004.838146openalex-snapshot
A positively self-feedbacked Hopfield neural network architecture for crossbar switching
inferred← citeshttps://doi.org/10.1109/tnn.2002.806621openalex-snapshot
A Noisy Self-Organizing Neural Network With Bifurcation Dynamics for Combinatorial Optimization
inferred← citeshttps://doi.org/10.1109/wcnc.2008.481openalex-snapshot
User-Centric Optimum Radio Access Selection in Heterogeneous Wireless Networks Based on Neural Network Dynamics
inferred← citeshttps://doi.org/10.1142/s0129065704002133openalex-snapshot
AN IMPROVED TRANSIENTLY CHAOTIC NEURAL NETWORK FOR THE MAXIMUM INDEPENDENT SET PROBLEM
inferred← citeshttps://doi.org/10.1142/s0129065707001056openalex-snapshot
A FLEXIBLE ANNEALING CHAOTIC NEURAL NETWORK TO MAXIMUM CLIQUE PROBLEM
inferred← citeshttps://doi.org/10.11499/sicep.2004.0_47_2openalex-snapshot
A fast and reliable approach to TSP using positively self-feedbacked Hopfield networks
inferred← citeshttps://doi.org/10.1162/08997660252741194openalex-snapshot
A Neural-Network-Based Approach to the Double Traveling Salesman Problem
inferred← citeshttps://doi.org/10.1162/0899766054796860openalex-snapshot
Optimization via Intermittency with a Self-Organizing Neural Network
inferred← citeshttps://doi.org/10.1162/neco.1996.8.5.1041openalex-snapshot
A Novel Optimizing Network Architecture with Applications
inferred← citeshttps://doi.org/10.1201/9781420050646.ptfopenalex-snapshot
Neural Network Applications
inferred← citeshttps://doi.org/10.1287/ijoc.11.1.15openalex-snapshot
Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research
inferred← citeshttps://doi.org/10.1287/ijoc.8.2.103openalex-snapshot
Application of an Annealed Neural Network to a Timetabling Problem
inferred← citeshttps://doi.org/10.1371/journal.pone.0224552openalex-snapshot
Genetic codes optimized as a traveling salesman problem
inferred← citeshttps://doi.org/10.15099/00004626openalex-snapshot
A New Method to Solve the Constraint Satisfaction Problem Using the Hopfield Neural Network
inferred← citeshttps://doi.org/10.1541/ieejeiss.123.355openalex-snapshot
A Saturation Computation Method of Artificial Binary Neural Networks for Combinatorial Optimization Problems.
inferred← citeshttps://doi.org/10.1541/ieejeiss.123.86openalex-snapshot
Learning Method of Hopfield Neural Network and Its Application to Traveling Salesman Problem.
inferred← citeshttps://doi.org/10.1541/ieejeiss.124.2155openalex-snapshot
A Modified Hopfield Neural Network for the Minimum Vertex Cover Problem
inferred← citeshttps://doi.org/10.1541/ieejeiss.124.2162openalex-snapshot
An Improved Transiently Chaotic Neural Network with Application to the Maximum Clique Problems
inferred← citeshttps://doi.org/10.1541/ieejeiss.124.2353openalex-snapshot
A Fast and Reliable Approach to TSP using Positively Self-feedbacked Hopfield Networks
inferred← citeshttps://doi.org/10.1541/ieejeiss.128.94openalex-snapshot
An Improved Maximum Neural Network with Stochastic Dynamics Characteristic for Maximum Clique Problem
inferred← citeshttps://doi.org/10.1587/bplus.14.119openalex-snapshot
コヒーレントイジングマシンを用いた組合せ最適化と無線 通信への応用
inferred← citeshttps://doi.org/10.1587/essfr.11.2_113openalex-snapshot
Combinatorial Optimization by Quantum Neural Networks and Its Applications
inferred← citeshttps://doi.org/10.1587/transcom.2020nvi0002openalex-snapshot
Optimization by Neural Networks in the Coherent Ising Machine and its Application to Wireless Communication Systems
inferred← citeshttps://doi.org/10.1615/faim1996.100openalex-snapshot
HOPFIELD NEURAL NETWORK FOR A TRANSNATIONAL ENTERPRISE'S MANUFACTURING NETWORK
inferred← citeshttps://doi.org/10.2172/1221961openalex-snapshot
Measurements of Polarization Transfers in Real Compton Scattering by a proton target at JLAB. A new source of information on the 3D shape of the nucleon
inferred← citeshttps://doi.org/10.2478/v10061-010-0028-zopenalex-snapshot
Application of a Modified Hopfield Network to the Traveling Salesman Problem
inferred← citeshttps://doi.org/10.25046/aj040334openalex-snapshot
A Support Vector Machine Cost Function in Simulated Annealing for Network Intrusion Detection
inferred← citeshttps://doi.org/10.25148/etd.fi15101396openalex-snapshot
A hierarchical approach for solving the large-scale traveling salesman problem
inferred← citeshttps://doi.org/10.3233/idt-2010-0090openalex-snapshot
Design and implementation of a user-centric access point selection algorithm based on mutually connected neural networks
inferred← citeshttps://doi.org/10.3389/fnins.2016.00118openalex-snapshot
Solving Constraint Satisfaction Problems with Networks of Spiking Neurons
inferred← citeshttps://doi.org/10.3390/e23040456openalex-snapshot
Single Neuronal Dynamical System in Self-Feedbacked Hopfield Networks and Its Application in Image Encryption
inferred← citeshttps://doi.org/10.4218/etrij.10.0109.0533openalex-snapshot
Optimized Local Relocation for VLSI Circuit Modification Using Mean-Field Annealing
inferred← citeshttps://doi.org/10.48550/arxiv.2006.10643openalex-snapshot
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs
inferred← citeshttps://doi.org/10.5772/52092openalex-snapshot
Mean Field Annealing Based Techniques for Resolving VLSI Automatic Design Problems
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