Iterative Selection of Unknown Weights in Direct Weight Optimization Identification

Abstract EN

To the direct weight optimization identification of the nonlinear system, we add some linear terms about input sequences in the former linear affine function so as to approximate the nonlinear property.

To choose the two classes of unknown weights in the more linear terms, this paper derives the detailed process on how to choose these unknown weights from theoretical analysis and engineering practice, respectively, and makes sure of their key roles between the unknown weights.

From the theoretical analysis, the added unknown weights’ auxiliary role can be known in the whole process of approximating the nonlinear system.

From the practical analysis, we learn how to transform one complex optimization problem to its corresponding common quadratic program problem.

Then, the common quadratic program problem can be solved by the basic interior point method.

Finally, the efficiency and possibility of the proposed strategies can be confirmed by the simulation results.