Abstract
The authors present a neural network structure whose nodes reflect a Gaussian-type activation function. Three learning algorithms are introduced and compared: the Gaussian perceptron learning (GPL) algorithm, which is based on the conventional perceptron convergence procedure; the least-squares error (LSE) algorithm, which follows the classical steepest descent approach; and the least-log squares error (LLSE) algorithm, which is a gradient method on a log objective function. In particular, the convergence of the GPL algorithm is proved. The performance of each algorithm is demonstrated through benchmark problems. >
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