A Devs-Based Ann Training and Prediction Platform

Adelani, David Ifeoluwa (2014-12-07)


The artificial intelligence (AI) domain grows every day with new algorithms and architectures. Artificial Neural Networks (ANNs), a branch of AI has become a very interesting domain since the eighties when the back-propagation learning algorithm and the feed-forward architecture were first introduced. As time passed, ANNs were able to solve non-linear problems, and were being used in classification, prediction, and representation of complex systems. However, ANN uses a black box learning approach – which makes it impossible to interpret the relationship between the input and the output. Discrete Event System Specification (DEVS) is a mathematical well-defined formalism that can be used to model dynamic systems in a hierarchical and modular manner; it can automatically generate simulators for the described DEVS models. Combining ANN and DEVS, we can model the complex configuration of ANNs and express its internal workings. In this thesis, we are extending the DEVS-Based ANN proposed by Toma et al [1] for comparing multiple configuration parameters and learning algorithms. The DEVS model is described using a visual modeling language known as High Level Language Specification (HiLLS) for a clear understanding. This approach will help users and algorithm developers to test and compare different algorithm implementations and parameter configurations of ANN.