Design of Neural Networks

Abstract

Artificial Neural Networks (ANN) and Deep Learning (DL) are used to solve complex problems including image recognition, speech recognition and have applications in new technologies for autonomous driving, facial recognition, detecting cancers from imaging samples among others. Various design considerations are involved in the design, training, and testing of Artificial Neural Networks (ANN). These include the design of the input/output layer, the structure and number of hidden layers, the data/data-structures of variables, the transformative functions embedded in the network, the optimizers being considered, the learning rate and its systematic adjustment, the prudent usage of dropout, the parallelism-related batch-size, the number of epochs, the adaptive logic for systematically changing the network for better fit, etc. While all these methods and techniques are sensible and relevant, there lacks an overarching framework for the needed design. This paper considers the design of an ANN from an Axiomatic Design (AD) perspective that parallels the biological inspiration for ANN’s in the first place, i.e., the brain. The axiomatic design approach is used for explicating and extricating the form, function, and adaptive evolution of the underlying network.