Abstract
The black box model of a dynamic system usually consists of just input and output. There is no correlation or coupling between the input and output. This paper proposes a self-coupling black box model method to realize the coupling between its input and output by introducing “virtual variables” to the black box model of a dynamic system considering the advantages of artificial neural network (ANN) in the system. The ANN is used for black box modeling. The modeling process of the self-coupling black box is illustrated through the simulation models and simulation analysis of the particle settlement process and the Unmanned Undersea Vehicle (UUV) launching process. By comparing its result with the result of the standard black box model, the advantages and disadvantages of the self-coupling and standard black box models in the calculation of accuracy are analyzed.
Highlights
At present, the research on modeling methods of the black box model mainly focuses on accurately realizing the mapping relationship between input and output
In order to solve the specific engineering problems, many scholars have adopted a variety of methods to build the black box model, such as autoregressive public model (ARX), transfer function model (TF), state space model (SS), and output error model (OE) [4,5,6,7,8]
Research studies on black box modeling methods mainly focus on accurately realizing the implicit relationship. ere is a lack of research on the correlation between the input and the output
Summary
When building the self-coupling black box model of a dynamic system, it is necessary to analyze the relationship between the variables in the system. E integral or differential quantities are introduced in the black box model as “virtual quantities.” e key variables and “virtual quantities” are considered as the output and input of the model, respectively. To further illustrate the principle of the self-coupling black box model, a system with four variables is illustrated [23] which includes one input variable A1(t) and three system variables X1(t), X2(t), and X3(t). (3) According to the self-coupling black box model, distinct test results are selected to fit the neural network model (the learning sample may be adjusted according to the results in the fourth step, i.e., different test data are adopted). If the result is not precise, steps 2, 3, 4, and 5 are repeated, and the self-coupling black box model is built
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