Softmax to Softassign: neural network algorithms for combinatorial optimization
Journal of Artificial Neural Networks - Special issue: neural networks for optimization
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
Manufacturing cell formation using a new self-organizing neural network
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Optimization via Intermittency with a Self-Organizing Neural Network
Neural Computation
Performance-enhancing bifurcations in a self-organising neural network
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Neural techniques for combinatorial optimization with applications
IEEE Transactions on Neural Networks
A noisy self-organizing neural network with bifurcation dynamics for combinatorial optimization
IEEE Transactions on Neural Networks
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Self-organizing neural networks (SONN) driven by softmax weight renormalization are capable of finding high quality solutions of difficult assignment optimization problems. The renormalization is shaped by a temperature parameter - as the system cools down the assignment weights become increasingly crisp. It has been recently observed that there exists a critical temperature setting at which SONN is capable of powerful intermittent search through a multitude of high quality solutions represented as meta-stable states of SONN adaptation dynamics. The critical temperature depends on the problem size. It has been hypothesized that the intermittent search by SONN can occur only at temperatures close to the first (symmetry breaking) bifurcation temperature of the autonomous renormalization dynamics. In this paper we provide a rigorous support for the hypothesis by studying stability types of SONN renormalization equilibria.