A Modeling Method for Uncertain Systems Based on Interval Feedback Neural Networks

Abstract

The invention provides an uncertain system modeling method based on an interval feedback neural network, and relates to the technical field of system modeling in industrial processes. The method includes: collecting the actual input and output data pairs of the system; collecting the system data pairs under the UBB condition; normalizing the actual point value input and the corresponding actual interval output data; using the normalized data as the training data pair interval The feedback neural network is trained offline to obtain the trained interval feedback neural network; the trained interval feedback neural network is tested with test samples and the output value prediction is completed. The present invention provides an uncertain system modeling method based on interval feedback neural network, utilizes the nonlinear approximation ability of interval feedback neural network, and adopts the gradient descent algorithm based on error backpropagation for network weight learning, avoiding neuron It is widely applicable to the modeling process of high-order dynamic systems with unknown but bounded errors.

Description

technical field [0001] The invention relates to the technical field of system modeling in industrial processes, in particular to an uncertain system modeling method based on an interval feedback neural network. Background technique [0002] In the actual system, due to the incompleteness of measurement and estimation, the disturbance and uncertainty in the industrial process, the obtained data are often inaccurate, and the equivalent model of the system determined by the input and output data is not accurate enough. Therefore, it is more reasonable to use an uncertain system model to describe an actual process. Expressing uncertainty as interval numbers is a simple and effective way to deal with it, and a system with interval parameters is called an interval system. If a deterministic system model is used to describe, there will inevitably be a certain deviation between the actual system and this model. [0003] The system description based on the unknown but bounded error...

AI Technical Summary

Problems solved by technology

Interval feedback neural network can solve the problem of high-order dynamic system modeling by feedforward neural network due to its own structure of memory and adaptability to time-varying characteristics, so it can be used as an effective means of modeling uncertain systems

Images