Abstract
An improved compound gradient vector based a fast convergent NN online training weight update scheme is proposed in this paper. The convergent analysis indicates that because the compound gradient vector is employed during the weight update, the convergent speed of the presented algorithm is faster than the back propagation (BP) algorithm. In this scheme an adaptive learning factor is introduced in which the global convergence is obtained, and the convergence procedure on plateau and flat bottom area can speed up. Some simulations have been conducted and the results demonstrate the satisfactory convergent performance and strong robustness are obtained using the improved compound gradient vector NN online learning scheme for real time control involving uncertainty parameter plant.
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Chen, Z., Dong, C., Zhou, Q., Zhang, S. (2003). An Improved Compound Gradient Vector Based Neural Network On-Line Training Algorithm. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_32
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DOI: https://doi.org/10.1007/3-540-45034-3_32
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