Abstract
A new neural network online training weight update scheme based on the use of a compound gradient vector is presented 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 standard BP algorithm. The comprehensive parameter adaptation and the saturation compensation approaches that are introduced in the scheme enhance convergent performance. Several simulations have been conducted and the results demonstrate the satisfactory convergent performance and strong robustness obtained using the improved neural networks online learning scheme for real time control involving uncertainty parameters.
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Chen, Z., Li, J., Yue, Y., Gao, Q., Zhao, H., Xu, Z. (2002). A Neural Network Online Training Algorithm Based on Compound Gradient Vector. In: McKay, B., Slaney, J. (eds) AI 2002: Advances in Artificial Intelligence. AI 2002. Lecture Notes in Computer Science(), vol 2557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36187-1_33
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DOI: https://doi.org/10.1007/3-540-36187-1_33
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