Associativity, auto-reversibility and question-answering on q'tron neural networks

Authors:
Tai-Wen Yue;Mei-Ching Chen
Affiliations:
Dept. of Computer Science and Engineering, Tatung University, Taipei, Taiwan, R.O.C.;Dept. of Computer Science and Engineering, Tatung University, Taipei, Taiwan, R.O.C.
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

Citing 2
Cited 1

Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing

Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Q'tron Neural Networks for Constraint Satisfaction

HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems

Sudoku solver by q’tron neural networks

ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Associativity, auto-reversibility and question-answering are the three intrinsic functions to be investigated for the proposed Q'tron Neural Network (NN) model. A Q'tron NN possesses these functions due to its property of local-minima free if it is built as a known-energy system which is equipped with the proposed persistent noise-injection mechanism. The so-built Q'tron NN, as a result, will settle down if and only if it ‘feels' feasible, i.e., the energy of its state has been low enough truly. With such a nature, the NN is able to accommodate itself ‘everywhere' to reach a feasible state autonomously. Three examples, i.e., an associative adder, an N-queen solver, and a pattern recognizer are demonstrated in this paper to highlight the concept.