A new algorithm for RNA secondary structure prediction using improved transiently chaotic neural network

Authors:
Yanqiu Che;Zheng Tang;Qiping Cao;Hongwei Dai
Affiliations:
Department of Intellectual Information Systems Engineering, Toyama University, Japan;Department of Intellectual Information Systems Engineering, Toyama University, Japan;Tateyama Institute of System, Japan;Department of Intellectual Information Systems Engineering, Toyama University, Japan
Venue:
MATH'06 Proceedings of the 10th WSEAS International Conference on APPLIED MATHEMATICS
Year:
2006

Citing 4
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Abstract

RNA secondary structure prediction is a computationally feasible and broadly studied problem. It can be considered as the combinatorial optimization problem. In this paper, we propose an improved transiently chaotic neural network (TCNN) for RNA secondary structure prediction. In the improved model, a variable p(t) called the acceptance probability of chaos is introduced into the original TCNN model. Variable p(t) is used to decide if the chaos term will be calculated or not. With variable p(t), the network can be speeded up to converge to a fixed point with fewer iterations. The improved TCNN is analyzed theoretically and evaluated experimentally through predicting RNA secondary structure. The simulation results show that the improved transiently chaotic neural network can reach stable state with fewer steps than the original transiently chaotic neural network.