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This modification improves the approximation performances and lightens burdens imposed on a network designer in the design process of SORN.
An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control.
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The NN is used to approximate the unknown nonlinear function due to its good approximation performance.
Four candidate approximation functions were identified and their approximation performance analyzed.
The approximation performance is measured by mean errors (e.g., integrated or maximal quadratic mean errors).
The proposed method presents similar or better approximation performance when compared to the other four methods.
The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance.
Numerical simulations are carried out to investigate various array configurations in relation to approximation performance.
Using Ackley's path function, the approximation performance of WNN is first tested in comparison with BPN.
Furthermore, the structure and parameter learning are designed in the ASCFNN identifier to achieve favorable approximation performance.
If the approximation performance of the SCNN is inadequate, the SCNN can create new hidden neurons to increase learning ability.
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