A New Training Method based on Black Hole Algorithm for Convolutional Neural Network

One of the commonly used neural networks currently is the Convolutional Neural Networks (CNN). They have different applications but have recently been proven useful in deep learning. With its continued growth in the more convoluted domains, the difficulty of its training process is equally increasing. As such, several hybrid algorithms have been developed and implemented to solve this problem. This paper proposes the use of the Black Hole (BH) algorithm to train CNN in a bid to improve its performance by avoiding local minima entrapment. The performance of the new training algorithm was compared to the currently used training algorithms in terms of the convergence analysis, computational accuracy and cost using a benchmark problem specific to Optical Character Recognition (OCR) applications.