A DEVS-BASED ANN TRAINING AND PREDICTION PLATFORM

📄 Project Abstract

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 ...