A Hybrid Training Algorithm for Continuum Deep Learning Neuro-Skin Neural Network
Abstract
In this brief paper, a learning algorithm is developed for Deep Learning NeuroSkin Neural Network to improve their learning properties. Neuroskin is a new type of neural network presented recently by the authors. It is comprised of a cellular membrane which has a neuron attached to each cell. The neuron is the cells nucleus. A neuroskin is modelled using finite elements. Each element of the finite element represents a cell. Each cells neuron has dendritic fibers which connects it to the nodes of the cell. On the other hand, its axon is connected to the nodes of a number of different neurons. The neuroskin is trained to contract upon receiving an input. The learning takes place during updating iterations using sensitivity analysis. It is shown that while the neuroskin cannot present the desirable response, it improves gradually to the desired level.
Cite
@article{arxiv.2302.01819,
title = {A Hybrid Training Algorithm for Continuum Deep Learning Neuro-Skin Neural Network},
author = {Mehrdad Shafiei Dizaji},
journal= {arXiv preprint arXiv:2302.01819},
year = {2023}
}
Comments
9 pages, 5 figures. arXiv admin note: substantial text overlap with arXiv:2007.04796