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In this paper, a new back propagation (BP) algorithm with adaptive momentum is proposed, where the momentum coefficient is adjusted iteratively based on the current descent direction and the weight increment in the last iteration. A convergence result of the algorithm is presented when it is used for training feed forward neural networks (FNNs) with a(More)
A gradient method with momentum for two-layer feedforward neural networks is considered. The learning rate is set to be a constant and the momentum factor an adaptive variable. Both the weak and strong convergence results are proved, as well as the convergence rates for the error function and for the weight. Compared to the existing convergence results, our(More)
In this paper, a squared penalty term is added to the conventional error function to improve the generalization of neural networks. A weight boundedness theorem and two convergence theorems are proved for the gradient learning algorithm with penalty when it is used for training a two-layer feedforward neural network. To illustrate above theoretical(More)
To improve the jetting performance of liquid metals, an electromagnetic micro-jetting (EMJ) valve that realizes drop-on-demand (DOD) jetting while not involving any valve core or moving parts was designed. The influence of the lead angle of the nozzle on the jetting of liquid metal gallium (Ga) was investigated. It was found that the Lorentz force component(More)