Abstract
System identification plays an important role in improving the structure and parameters of a system, but there are many problems encountered in actual operation. The identification of dynamic systems is not as simple as it is for static systems; thus, choosing effective model structures and parameters is the key to solving this problem. This paper proposes a novel algorithm based on a combination of a broad learning system (BLS) and particle swarm optimization (PSO) to identify nonlinear dynamical systems. The proposed method first uses the dimension expansion of the data set as the input of the BLS and then optimizes the model weight by the PSO algorithm. To verify the effectiveness of our proposal, we use four second-order systems for simulation experiments. The simulation results clearly show the efficiency and anti-interference ability of the proposed method.
Highlights
In the past several years, dynamic systems have been used in areas such as communication, control, and pattern recognition
The system identification method is used to establish the model of the controlled system, which can be used to analyze the performance and dynamic and static response characteristics of the system to improve the structure and parameters of the system; system identification has been widely considered by engineers, but they have faced a number of problems in each of these application areas
The parameter identification method of the nonlinear dynamic system proposed in this paper is based on the particle swarm optimization (PSO) algorithm
Summary
In the past several years, dynamic systems have been used in areas such as communication, control, and pattern recognition. The parameter identification method of the nonlinear dynamic system proposed in this paper is based on the PSO algorithm. The original PSO is very simple, with only a few parameters to adjust, it provides better performance in computing speed, computing accuracy, and memory size compared with other methods such as machine learning, neural network learning, and genetic computation It has received much more attention in solving optimization problems [22]–[24]. We used PSO to optimize the output weight W2 to improve the accuracy of the whole model for nonlinear system identification. DESIGNING STEPS FOR PSO-BLS PSO is a biology-inspired evolutionary computation algorithm that was first introduced by Kenndy and Eberhart [31] It is a population-based stochastic optimization technique inspired by the boid model. According to the size of the defined cost function, each particle optimal solution (gbest) and population optimal solution (zbest) is recorded for the particle velocity update
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