Generalisation of Feed-Forward Neural Networks and Recurrent Neural Networks

Abstract

This paper presents an in-depth analysis of Feed-Forward Neural Networks (FNNs) and Recurrent Neural Networks (RNNs), two powerful models in the field of artificial intelligence. Understanding these models and their applications is crucial for harnessing their potential. The study addresses the need to comprehend the unique characteristics and architectures of FNNs and RNNs. These models excel at processing sequential and temporal data, making them indispensable in tasks. Furthermore, the paper emphasises the importance of variables in FNNs and proposes a novel method to rank the importance of independent variables in predicting the output variable. By understanding the relationship between inputs and outputs, valuable insights can be gained into the underlying patterns and mechanisms driving the system being modelled. Additionally, the research explores the impact of initial weights on model performance. Contrary to conventional beliefs, the study provides evidence that neural networks with random weights can achieve competitive performance, particularly in situations with limited training datasets. This finding challenges the traditional notion that careful initialization is necessary for neural networks to perform well. In summary, this paper provides a comprehensive analysis of FNNs and RNNs while highlighting the importance of understanding the relationship between variables and the impact of initial weights on model performance. By shedding light on these crucial aspects, this research contributes to the advancement and effective utilisation of neural networks, paving the way for improved predictions and insights in various domains.