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Your search keyword '"Zurada, Jacek M."' showing total 2 results
Start Over Author "Zurada, Jacek M." Publication Year Range Last 3 years
2 results on '"Zurada, Jacek M."'

1. Convergence analysis for sigma-pi-sigma neural network based on some relaxed conditions.

Author

Fan, Qinwei, Kang, Qian, and Zurada, Jacek M.

Subjects
*ERROR functions, *MACHINE learning, *SET functions, *POINT set theory
Abstract

This work proves the deterministic convergence of the Sigma-Pi-Sigma neural network based on the batch gradient learning algorithm under certain relaxed conditions. We establish strong and weak convergence results and prove that the error function decreases monotonically and tends to zero. The boundedness of weights is also proved simply and efficiently. In contrast to the usual requirements for the boundedness of weights, this work shows that such weight boundedness is no more one of the necessary conditions for ensuring convergence. In addition, we also show that the requirements on the learning rate and the stationary point set of the error function can be relaxed. Finally, the effectiveness of the proposed algorithm is validated by numerical experiments, followed by brief conclusions. [ABSTRACT FROM AUTHOR]

Published
2022
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2. A pruning algorithm with relaxed conditions for high-order neural networks based on smoothing group [formula omitted] regularization and adaptive momentum.

Author

Kang, Qian, Fan, Qinwei, Zurada, Jacek M., and Huang, Tingwen

Subjects
*HOPFIELD networks, *ABSOLUTE value, *SMOOTHNESS of functions, *ALGORITHMS
Abstract

To enhance the sparseness of the network, improve its generalization ability and accelerate its training, we propose a novel pruning approach for sigma-pi-sigma neural network (SPSNN) under the relaxed condition by adding smoothing group L 1 / 2 regularization and adaptive momentum. The main strength of this method is that it can prune both the redundant nodes between groups in the network, and also the redundant weights of the non-redundant nodes within the group, so as to achieve the sparseness of the network. Another strength is that the non-smooth absolute value function in the traditional L 1 / 2 regularization method is replaced by a smooth function. This reduces the oscillations of learning and enables us to more effectively prove the convergence of the proposed algorithm. Finally, the numerical simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Published
2022
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