Abstract:

In order to curb the continuous growing of model network size and effectively adapt to real-time system variation, an online sparse kernel extreme learning machine (KELM) with adaptive regulation factor is proposed to model time-varying nonlinear systems. Construction of a new objective function makes the model have different structural risks in different nonlinear regions and ensures the regulation factor vary over time with the time-varying nonlinear dynamics. A three-step solving method is used to determine the sparse dictionary and current optimal regulation factor. The proposed method has the capability of online updating both the kernel weight coefficient and the regulation factor vector. The effectiveness of the proposed method is demonstrated through applying it to the modeling of a practical case. Comparisons between the proposed method and existing KELM-based modeling methods indicate that the proposed method can effectively improve modeling accuracy and has better stability.