On the stability of the travelling salesman problem algorithm of Hopfield and Tank
Biological Cybernetics
Fundamentals of speech recognition
Fundamentals of speech recognition
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Chaotic neural networks with reinforced self-feedbacks and its application to N-Queen problem
Mathematics and Computers in Simulation
A hybrid chaotic genetic algorithm for short-term hydro system scheduling
Mathematics and Computers in Simulation
Self-organised dynamic recognition states for chaotic neural networks
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Noisy Chaotic Neural Networks for Solving Combinatorial Optimization Problems
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Neural Networks - 2005 Special issue: IJCNN 2005
Chaotic dynamic characteristics in swarm intelligence
Applied Soft Computing
2008 Special Issue: Threshold control of chaotic neural network
Neural Networks
Associative memory with a controlled chaotic neural network
Neurocomputing
A study of the transiently chaotic neural network for combinatorial optimization
Mathematical and Computer Modelling: An International Journal
Critical temperature of the transiently chaotic neural network
Mathematical and Computer Modelling: An International Journal
On chaotic simulated annealing
IEEE Transactions on Neural Networks
The hysteretic Hopfield neural network
IEEE Transactions on Neural Networks
Chaotic simulated annealing with decaying chaotic noise
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Dynamic programming matching (DPM) is a technique that finds an optimal match between two sequences of feature vectors allowing for stretched and compressed sections of the sequence. The purpose of this study is to formulate the matching problem as an optimization task and carry out this optimization problem by means of a chaotic neural network. The proposed method uses TCNN, a Hopfield neural network with decaying self-feedback, to find the best-matching (i.e., the lowest global distance) path between an input and a template. Experimental results show a very good performance for the proposed algorithm in pattern recognition tasks.