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Parallel Implementation Of A Recursive Least Squares Neural Network Training Method On The Intel Ipsc/2, James Edward Steck, Bruce M. Mcmillin, K. Krishnamurthy, M. Reza Ashouri, Gary G. Leininger
Parallel Implementation Of A Recursive Least Squares Neural Network Training Method On The Intel Ipsc/2, James Edward Steck, Bruce M. Mcmillin, K. Krishnamurthy, M. Reza Ashouri, Gary G. Leininger
Computer Science Faculty Research & Creative Works
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 …
Planning Optimal Robot Trajectories By Cell Mapping, W. H. Zhu, Ming-Chuan Leu
Planning Optimal Robot Trajectories By Cell Mapping, W. H. Zhu, Ming-Chuan Leu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A cell-mapping method is introduced for planning global trajectories of robotic manipulators in cases where the cell space is composed of combination pairs of plane cells. With the proposed method, optimal trajectory problems in the free field and in the obstacle-constrained field are studied. Two numerical examples are given to show the obtained optimal trajectories and controls.