2 research outputs found
Improving Neural Networks by Adopting Amplifying and Attenuating Neurons
In the present study, an amplifying neuron and attenuating neuron, which can
be easily implemented into neural networks without any significant additional
computational effort, are proposed. The activated output value is squared for
the amplifying neuron, while the value becomes its reciprocal for the
attenuating one. Theoretically, the order of neural networks increases when the
amplifying neuron is placed in the hidden layer. The performance assessments of
neural networks were conducted to verify that the amplifying and attenuating
neurons enhance the performance of neural networks. From the numerical
experiments, it was revealed that the neural networks that contain the
amplifying and attenuating neurons yield more accurate results, compared to
those without them.Comment: 8 pages, the figure 6-(b) and (c) were exchange
An Approach to Stable Gradient Descent Adaptation of Higher-Order Neural Units
Stability evaluation of a weight-update system of higher-order neural units
(HONUs) with polynomial aggregation of neural inputs (also known as classes of
polynomial neural networks) for adaptation of both feedforward and recurrent
HONUs by a gradient descent method is introduced. An essential core of the
approach is based on spectral radius of a weight-update system, and it allows
stability monitoring and its maintenance at every adaptation step individually.
Assuring stability of the weight-update system (at every single adaptation
step) naturally results in adaptation stability of the whole neural
architecture that adapts to target data. As an aside, the used approach
highlights the fact that the weight optimization of HONU is a linear problem,
so the proposed approach can be generally extended to any neural architecture
that is linear in its adaptable parameters.Comment: 2016, 13 page