An algorithm based on the Marquardt-Levenberg least-square optimization method has been shown by S. Kollias and D. Anastassiou (IEEE Trans. on Circuits Syst. vol.36, no.8, p.1092-101, Aug. 1989) to be a much more efficient training method than gradient descent, when applied to some small feedforward neural networks. Yet, for many applications, the increase in computational complexity of the method outweighs any gain in learning rate obtained over current training methods. However, the least-squares method can be more efficiently implemented on parallel architectures than standard methods. This is demonstrated by comparing computation times and learning rates for the least-squares method implemented on 1, 2, 4, 8, and 16 processors on an Intel iPSC/2 multicomputer. Two applications which demonstrate the faster real-time learning rate of the last-squares method over than of gradient descent are given
J. E. Steck et al., "Parallel Implementation of a Recursive Least Squares Neural Network Training Method on the Intel IPSC/2," Proceedings of the IJCNN International Joint Conference on Neural Networks (1990, San Diego, CA), pp. 631-636, Institute of Electrical and Electronics Engineers (IEEE), Jun 1990.
The definitive version is available at https://doi.org/10.1109/IJCNN.1990.137641
IJCNN International Joint Conference on Neural Networks (1990: Jun. 17-21, San Diego, CA)
Mechanical and Aerospace Engineering
IEEE Neural Networks Council
International Neural Network Society
Keywords and Phrases
Intel IPSC/2; Marquardt-Levenberg Least-Square Optimization Method; Computation Times; Convergence; Learning Rates; Learning Systems; Least Squares Approximations; Neural Nets; Optimisation; Parallel Architectures; Parallel Processing; Recursive Least Squares Neural Network Training; Supervised Learning
Article - Conference proceedings
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