Analyzing the Impact of Network Structures on Reservoir Computing

Abstract

Analyzing the Impact of Reservoir Network Structures on Reservoir Computing Artificial neural networks have become indispensable, efficient, and powerful tools in modern human life. Our research focused on exploring the capabilities of these networks and understanding their various applications. Specifically, we emphasized Reservoir Computing, a neural network model known for its rapid learning process and strong predictive power. A key feature of Reservoir Computing is its use of a fixed reservoir network during training, typically constructed using an Erdős–Rényi (ER) random network. The main goal of our study was to investigate how modifying the positive sign ratio of weights within the reservoir network affects its dynamic properties and, consequently, the predictive performance of the Reservoir Computing model. By systematically adjusting the positive sign ratio, we observed and analyzed changes in the network's dynamics and assessed the impact of these adjustments on the model's overall predictive capability. Moreover, we focused on predicting complex, unpredictable chaotic systems. In our research, we successfully predicted systems such as the Lorenz and Mackey-Glass systems. These systems exhibit sensitive and challenging dynamic behaviors, making their accurate prediction a critical test for the performance of Reservoir Computing models. We measured the Shannon entropy within the reservoir network to assess the amount of information it holds and analyzed the correlation between the amount of information and the performance of the Reservoir Computing model. This analysis revealed that reservoir networks with certain ranges of positive sign ratios held more information, which in turn enhanced the predictive performance of the Reservoir Computing models. This discovery provides crucial insights into optimizing the structure and dynamics of reservoir networks and expands the potential applications of neural network models in predicting and analyzing complex systems. In conclusion, our research explores various aspects of artificial neural networks and Reservoir Computing and demonstrates how they can be applied to solve real-world problems. This study contributes significantly to the advancement of neural network technologies and to the understanding and prediction of complex dynamic systems. To demonstrate the practical applications of neural networks, we conducted a project using a Feedforward Neural Network (FNN) to analyze olfactory sensor data. The goal of this project was to distinguish between various types and concentrations of gases, demonstrating the effectiveness of the FNN in processing complex data sets and solving real problems. Additionally, our research included a separate project that solved specific problems without relying on neural network- based methods. This project focused on separating and analyzing nano- patterned images similar to fingerprints, showing that certain types of problems can be solved efficiently and effectively without neural networks. This research makes a significant contribution to the fields of artificial intelligence and neural networks, clearly demonstrating the diversity of neural networks in solving a wide range of problems, from environmental sensing and gas detection to complex pattern recognition. Moreover, this study paves the way for future research into optimizing neural network structures and algorithms for various applications.