@inproceedings{f15ef21b783848b7bdcdfca6ed82a3b4,
title = "Development of quadratic neural unit with applications to pattern classification",
abstract = "The computational neural-network structures described in the literature are often based on the concept of linear neural units (LNUs). The biological neuron is a complex computing element, which performs more computations than just linear summation. The computational efficiency of the neural network depends on its structure and the training methods employed. Higher-order combinations of inputs and weights will yield higher neural performance. Here, a quadratic-neural unit (QNU) has been developed using a novel general matrix form of the quadratic operation. We have used the QNU for realizing different logic circuits.",
keywords = "Biological neural networks, Biology computing, Computational intelligence, Intelligent structures, Intelligent systems, Laboratories, Mechanical engineering, Multi-layer neural network, Neurons, Pattern classification",
author = "S. Redlapalli and Gupta, {M. M.} and Song, {K. Y.}",
note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; 4th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2003 ; Conference date: 21-09-2003 Through 24-09-2003",
year = "2003",
doi = "10.1109/ISUMA.2003.1236154",
language = "English",
series = "4th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2003",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "141--146",
editor = "Attoh-Okine, {Nii O.} and Ayyub, {Bilal M.}",
booktitle = "4th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2003",
address = "United States",
}