Generalized backpropagation algorithm for training second‐order neural networks

The artificial neural network is a popular framework in machine learning. To empower individual neurons, we recently suggested that the current type of neurons could be upgraded to second‐order counterparts, in which the linear operation between inputs to a neuron and the associated weights is repla...

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Vydáno v:International journal for numerical methods in biomedical engineering Ročník 34; číslo 5; s. e2956 - n/a
Hlavní autoři: Fan, Fenglei, Cong, Wenxiang, Wang, Ge
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Wiley Subscription Services, Inc 01.05.2018
Témata:
Algorithms
artificial neural network
Artificial neural networks
Back propagation
backpropagation (BP)
Fuzzy Logic
Learning algorithms
Machine Learning
Mathematical models
Neural networks
Neural Networks (Computer)
Neurons
second‐order neurons
artificial neural network
second-order neurons
backpropagation (BP)
ISSN:2040-7939, 2040-7947, 2040-7947
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Shrnutí:The artificial neural network is a popular framework in machine learning. To empower individual neurons, we recently suggested that the current type of neurons could be upgraded to second‐order counterparts, in which the linear operation between inputs to a neuron and the associated weights is replaced with a nonlinear quadratic operation. A single second‐order neurons already have a strong nonlinear modeling ability, such as implementing basic fuzzy logic operations. In this paper, we develop a general backpropagation algorithm to train the network consisting of second‐order neurons. The numerical studies are performed to verify the generalized backpropagation algorithm. The main contribution of this paper is to propose deep neural networks consisting of quadratic artificial neurons and an associated generalized backpropagation algorithm and illustrate such networks with a number of examples. Interestingly, each quadratic neuron can be interpreted as a fuzzy logic gate, and a neural network of quadratic neurons can be naturally interpreted as a deep fuzzy logic system. Hence, we suggest to understand and develop quadratic neural networks in light of fuzzy logic theory and techniques for applications in which fuzzy logic is relevant.
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ISSN:2040-7939
2040-7947
2040-7947
DOI:10.1002/cnm.2956