A fast and efficient method for inferring structure and parameters of S-system models

Most previous methods of inferring the S-system models have a significant limitation. That is, the structure of S-system models is fixed, and the only goal is to optimize its parameters and coefficients. Because the number of S-system parameters is proportional to the square of the number of variable, a large number of S-system parameters need to be simultaneously estimated, when the number of variable is very large. This limit may lead to the overwhelming computational complexities. To overcome this limitation we propose the restricted additive tree model for inferring the S-system models. In this approach, the evolution algorithm based on tree-structure and the particle swarm optimization (PSO) are employed to evolve the structure and the parameters of the S-system models, respectively. And the partitioning strategy is used to reduce the search space. We make three experiments and Simulation results using both synthetic data and real microarray measurements show that the structures and parameters of the S-system models can be identified correctly, which demonstrate the effectiveness of the proposed methods. The experiment results show that the structures and parameters of the S-system models can be identified correctly. And compared with other methods, the spent time is sharply reduced.