Detector neural network vs connectionist ANNs (original) (raw)

Neurocomputing, 2020

Abstract

Most widely used modern artificial neural networks are based on the connectionist paradigm of building and learning. The authors propose an alternative detector approach. The basis of this approach is the original architecture of the neural network, as well as a new procedure for its learning. The developed neural network is called the detector neural network. This network consists of two layers of neurons. The neurons of the first layer are called neurons-pre-detectors and they do not learn. They are designed to highlight the structural elements of recognizable images, as well as to determine their measured parameters. The types of structural elements and their parameters are set a priori and depend on the type and complexity of recognizable images. Neurons of the second layer can be trained. They recognize individual complex images. These neurons are called neurons-detectors (ND). The model of the ND is significantly different from all known models of neurons and has important fea...

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