Reference : Prototype Incorporated Emotional Neural Network (PI-EmNN)
Scientific journals : Article
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/32251
Prototype Incorporated Emotional Neural Network (PI-EmNN)
English
Oyedotun, Oyebade mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Khashman, Adnan mailto []
15-Aug-2017
IEEE Transactions on Neural Networks and Learning Systems
Yes
International
[en] Emotional neural network ; prototype learning ; classification
[en] Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ‘engineering’ prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, prototype learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype and adaptive learning theories. We refer to our new model as “PI-EmNN” (Prototype-Incorporated Emotional Neural Network). Furthermore, we apply the proposed model to two real-life challenging tasks, namely; static hand gesture recognition and face recognition, and compare the result to those obtained using the popular back propagation neural network (BPNN), emotional back propagation neural network (EmNN), deep networks and an exemplar classification model, k-nearest neighbor (k-NN).
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/32251
10.1109/TNNLS.2017.2730179

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