Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Backpropagation: theory, architectures, and applications
Backpropagation: theory, architectures, and applications
Fast learning in networks of locally-tuned processing units
Neural Computation
An optimized experimental protocol based on neuro-evolutionary algorithms
Artificial Intelligence in Medicine
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In this paper a new family of neural network named Sine Net (SN) is presented. It is characterized by the presence of a specific double non-linear relationship on the connections between nodes. This characteristic has some evident consequences on the properties of this network both on the computed function and on the behaviour of this network during the learning phase. The first part of the article is the presentation of SN within a theoretical and mathematical framework, in the last some interesting results on the application of SN on artificial and real data are illustrated, underlining the most relevant properties of this adaptive system.