Blood Cell Identification Using Emotional Neural Networks

Abstract

The idea of machines having emotions sounds like science fiction, however, few decades ago the idea of machines with intelligence seemed also like fiction, but today we are developing intelligent machines with successful applications. We have always over-looked the emotional factors during machine learning and decision making; however, it is quite conceivable to artificially model certain emotions in machine learning. This paper presents an emotional neural network (EmNN) that is based on the emotional back propagation (EmBP) learning algorithm. The EmNN has emotional weights and two emotional parameters; anxiety and confidence, which are updated during learning. The performances of the EmNN and a conventional BP-based neural network, using two to-pologies for each network, will be compared when applied to a blood cell type identifica-tion problem. Experimental results show that the additional emotional parameters and weights improved the identification rate as well as the classification time.