A Chaotic Maximum Neural Network for Maximum Clique Problem(Biocybernetics, Neurocomputing)
スポンサーリンク
概要
- 論文の詳細を見る
In this paper, based on maximum neural network, we propose a new parallel algorithm that can escape from local minima and has powerful ability of searching the globally optimal or near-optimum solution for the maximum clique problem (MCP). In graph theory a clique is a completely connected subgraph and the MCP is to find a clique of maximum size of a graph. The MCP is a classic optimization problem in computer science and in graph theory with many real-world applications, and is also known to be NP-complete. Lee and Takefuji have presented a very powerful neural approach called maximum neural network for this NP-complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to the local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm.
- 社団法人電子情報通信学会の論文
- 2004-07-01
著者
-
TANG Zheng
Faculty of Engineering, Toyama University
-
Wang Rong
The Faculty Of Engineering Fukui University
-
Tang Zheng
Faculty Of Engineering Miyazaki University
-
Wang R
Fukui Univ. Fukui‐shi Jpn
-
Wang R
Faculty Of Engineering Fukui University
-
WANG Jiahai
the Faculty of Engineering, Toyama University
-
WANG Ronglong
Faculty of Engineering, Fukui University
-
Wang Ronglong
Faculty Of Engineering Fukui University
-
WANG Jiahai
Faculty of Engineering, Toyama University
-
Wang Jiahai
Faculty Of Engineering Toyama University
関連論文
- Multilayer Network Learning Algorithm Based on Pattern Search Method(Neural Networks and Bioengineering)
- A Local Search Based Learning Method for Multiple-Valued Logic Networks(Neural Networks and Bioengineering)
- A Method of Learning for Multi-Layer Networks
- A Parallel Graph Planarization Algorithm Using Gradient Ascent Learning of Hopfield Network
- A Saturation Computation Method of Artificial Binary Neural Networks for Combinatorial Optimization Problems
- A Fast and Reliable Approach to TSP using Positively Self-feedbacked Hopfield Networks
- Objective Function Adjustment Algorithm for Combinatorial Optimization Problems(Numerical Analysis and Optimization)
- An Expanded Maximum Neural Network with Chaotic Dynamics for Cellular Radio Channel Assignment Problem(Nonlinear Problems)
- An Improved Artificial Immune Network Model(Neural Networks and Bioengineering)
- A Neural-based Algorithm for Topological Via-minimization Problem
- A New Method to Solve the Constraint Satisfaction Problem Using the Hopfield Neural Network
- An Artificial Immune Network with Multi-layered B Cells Architecture
- An Artificial Immune System Architecture and Its Applications(Neural Networks and Bioengineering)
- The Fuzzy Immune Network and Its Application to Pattern Recognition(Special Section on Papers Selected from ITC-CSCC 2002)
- Design and realization of a network security model
- Affinity Based Lateral Interaction Artificial Immune System(Human-computer Interaction)
- Avoiding the Local Minima Problem in Backpropagation Algorithm with Modified Error Function(Neural Networks and Bioengineering)
- An Engineering Immune Network Model for Pattern Recognition
- Pattern Classification Using A Fuzzy Immune Network Model
- D-2-6 A Parallel Direct Search Learning Algorithm for Feed-Forward Neural Networks
- An Improved Maximum Neural Network with Stochastic Dynamics Characteristic for Maximum Clique Problem
- A Near-Optimum Parallel Algorithm for a Graph Layout Problem(Neural Networks and Bioengineering)
- Learning Method of Hopfield Neural Network and Its Application to Traveling Salesman Problem (特集:論文誌C発刊30周年記念)
- A Multiple-Valued Immune Network and Its Applications
- Neuron-MOS Current Mirror Circuit and Its Application to Multi-Valued Logic (Special Issue on Multiple-Valued Logic and Its Applications)
- A 1-V, 1-V_ Input Range, Four-Quadrant Analog Multiplier Using Neuron-MOS Transistors
- Ultra-Low Power Two-MOS Virtual-Short Circuit and Its Application
- 自己学習ファジ-コントロ-ラ
- Design and Implementation of a Calibrating T-Model Neural-Based A/D Converter
- Hopfield Neural Network Learning Using Direct Gradient Descent of Energy Function
- Implementation of T-Model Neural-Based PCM Encoders Using MOS Charge-Mode Circuits
- A Learning Fuzzy Network and Its Applications to Inverted Pendulum System
- An Elastic Net Learning Algorithm for Edge Linking of Images
- Solving Maximum Cut Problem Using Improved Hop field Neural Network
- A Near-Optimum Parallel Algorithm for Bipartite Subgraph Problem Using the Hopfield Neural Network Learning
- Quantum Interference Crossover-Based Clonal Selection Algorithm and Its Application to Traveling Salesman Problem
- An Efficient Neural Algorithm for Two-layer Planarization Problem in Graph Drawing
- Maximum Neural Network with Nonlinear Self-Feedback and Its Application to Maximum Independent Set Problem
- An Expanded Lateral Interactive Clonal Selection Algorithm and Its Application
- Improved Clonal Selection Algorithm Combined with Ant Colony Optimization
- An Improved Clonal Selection Algorithm and Its Application to Traveling Salesman Problems(Neural Networks and Bioengineering)
- A Novel Clonal Selection Algorithm and Its Application to Traveling Salesman Problem(Neural Networks and Bioengineering)
- A stochastic dynamic local search method for learning Multiple-Valued Logic networks
- An Improved Artificial Immune System (AIS) by Considering Different Affinities among Th Cells and Antigens
- A Learning Algorithm of Elastic Net for Multiple Traveling Salesmen Problem
- Multiple-Valued Static Random-Access-Memory Design and Application : Special Issue on Multiple-Valued integrated Circuits
- An Efficient Algorithm for Minimum Vertex Cover Problem
- Two-Phase Pattern Search-based Learning Method for Multi-layer Neural Network
- A Chaotic Maximum Neural Network for Maximum Clique Problem(Biocybernetics, Neurocomputing)
- A New Parallel Algorithm Analogous to Elastic Net Method for Bipartite Subgraph Problem
- A Parallel Graph Planarization Algorithm Using Gradient Ascent Learning of Hopfield Network
- An Efficient Algorithm for Maximum Clique Problem Using Improved Hopfield Neural Network
- A Saturation Computation Method of Artificial Binary Neural Networks for Combinatorial Optimization Problems
- A Parallel Algorithm for Maximum Cut Problem Using Gradient Ascent Learning of Hopfield Neural Networks
- A Gradient Ascent Learning Algorithm in Weight Domain for Hopfield Neural Networks
- A Hopfield Network Learning Algorithm for Graph Planarization
- A Gradient Ascent Learning Algorithm for Elastic Nets
- A Modified Hopfield Neural Network for the Minimum Vertex Cover Problem
- An Improved Transiently Chaotic Neural Network with Application to the Maximum Clique Problems
- An Elastic Net Learning Algorithm for Edge Linking of Images(Neural Netoworks and Bioengineering)
- A Novel Maximum Neural Network with Stochastic Dynamics for N-Queens Problems
- A Child Verb Learning Model Based on Syntactic Bootstrapping
- Design and Implementations of a Learning T-Model Neural Network
- Investigation and Analysis of Hysteresis in Hopfield and T-Model Neural Networks
- T-Model Neural Network for PCM Encoding
- Stochastic Competitive Hopfield Network and Its Application to Maximum Clique Problem(Neural Networks and Bioengineering)