Inferring structure and parameters of dynamic system models simultaneously using swarm intelligence approaches

Abstract

Inferring dynamic system models from observed time course data is very challenging compared to static system identification tasks. Dynamic system models are complicated to infer due to the underlying large search space and high computational cost for simulation and verification. In this research we aim to infer both the structure and parameters of a dynamic system simultaneously by particle swarm optimization (PSO) improved by efficient stratified sampling approaches. More specifically, we enhance PSO with two modern stratified sampling techniques, i.e., Latin hyper cube sampling (LHS) and Latin hyper cube multi dimensional uniformity (LHSMDU). We propose and evaluate two novel swarm-inspired algorithms, LHS-PSO and LHSMDU-PSO, which can be used particularly to learn the model structure and parameters of complex dynamic systems efficiently. The performance of LHS-PSO and LHSMDU-PSO is further compared with the original PSO and genetic algorithm (GA). We chose real-world cancer biological model called Kinetochores to asses the learning performance of LHSMDU-PSO and LHS-PSO in comparison with GA and the original PSO. The experimental results show that LHSMDU-PSO can find promising models with reasonable parameters and structure, and it outperforms LHS-PSO, PSO, and GA in our experiments.

Surprisingly Popular Algorithm-based Adaptive Euclidean Distance Topology Learning PSO

“…Cause of the requirement of a higher computational, representation method of structure and design of the solution space, inferring the structure and parameters of the ordinary differential equations models simultaneously is a more challenging task [2,38]. To prove the effectiveness of SpadePSO, we infer the structure and parameters of the HIV model from scratch, which means we unknown anything about variables, parameters and interrelation between items in the initial stage.…”

“…Upper limit of the edges numbers in the Etmp vu lk Increasing velocity of u lk k Out-degree of each particle in the knowledge transfer topology in the initial state and practical problems, such as neural architecture search [1], ordinary differential equations optimization [2] and airfoil design [3]. Because of its simple principle, few parameters and fast convergence ability, particle swarm optimization (PSO) [4] is still an important tool for solving problems [5,6].…”

Research on Dynamic Balance of Spindle Rotor System Based on Particle Swarm Optimization

“…Díaz et al [16] proposed a novel method which could find the real modes of the rotor system through the coordinate transformation, which resulted in that the optimum extraction position of the balancing parameters can be determined. Besides, it has been demonstrated that the stability and efficiency of rotor system dynamic balancing could be improved through PSO algorithm [17][18][19].…”