Abstract
Due to the inevitable role of emotions in human learning and decision-making, different types of emotions in the form of emotional weights/neurons have also been considered in shallow neural networks. Emotional neural networks suffer from a low convergence rate as well as batch learning instability mainly because of the improper tuning of learning coefficients. To overcome these drawbacks, we introduced two solutions: (i) a heuristic upgrading method, inspiring by the behavior of dopamine secretion in the human brain, to adaptively regulate the learning rate based on positive and negative emotional states at each epoch and (ii) a stochastic learning technique to stabilize the learning process. The proposed dopamine based adaptive emotional neural network statistically outperforms state-of-the-art methods like emotional neural network, prototype-incorporated emotional neural network, multi-layer perceptron, and deep convolutional neural networks such as LeNet, AlexNet, DenseNet, MobileNet and EfficientNet in terms of different measures such as accuracy and convergence rate on several high dimensional and big datasets.