Prototype-Incorporated Emotional Neural Network

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 8; pp. 3560 - 3572
• Main Authors: Oyedotun, Oyebade K., Khashman, Adnan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Artificial neural networks; Back propagation networks; Biological neural networks; Cognition; Computer simulation; Emotional neural network (EmNN); Emotions; Face recognition; Feature extraction; Gesture recognition; hand-gesture recognition; Learning; Learning theory; neural network; Neural networks; Neurons; Pattern recognition; prototype learning; Prototypes; Psychology; Training
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2017.2730179

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, the 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 "prototype-incorporated EmNN". 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 BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.