Black - Box Modeling with State - Space Neural Networks
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چکیده
Neural network black-box modeling is usually performed using nonlinear input-output models. The goal of this paper is to show that there are advantages in using nonlinear state-space models, which constitute a larger class of nonlinear dynamical models, and their corresponding state-space neural predictors. We recall the fundamentals of both input-output and state-space black-box modeling, and show the state-space neural networks to be potentially more efficient and more parsimonious than their conventional input-output counterparts. This is examplified on simulated processes as well as on a real one, the hydraulic actuator of a robot arm.
منابع مشابه
Black-box Modeling with State-space Neural Networks
Neural network black-box modeling is usually performed using nonlinear inputoutput models. The goal of this paper is to show that there are advantages in using nonlinear state-space models, which constitute a larger class of nonlinear dynamical models, and their corresponding state-space neural predictors. We recall the fundamentals of both input-output and state-space black-box modeling, and s...
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