"Advancing Intelligent Systems with Neural Networks: Exploring Dynamic Systems and Computational Models for Enhanced Adaptability"

EasyChair Preprint 14361

Abstract

The rapid evolution of intelligent systems has underscored the need for advanced methodologies to enhance adaptability and performance. This paper explores the integration of neural networks into dynamic systems and computational models to foster more adaptable and robust intelligent systems. By leveraging state-of-the-art neural network architectures, including deep learning and reinforcement learning, we investigate how these technologies can be applied to model and predict complex, time-varying phenomena. Our approach emphasizes the development of dynamic systems that can self-adjust in response to changing conditions, improving both efficiency and resilience. Through a comprehensive analysis of case studies and experimental results, we demonstrate how neural networks can be harnessed to solve real-world problems with greater accuracy and flexibility. The findings contribute to a deeper understanding of how intelligent systems can evolve in response to new challenges, paving the way for future advancements in adaptive technologies and their applications across various domains.

Keyphrases: Information Systems, Machine Learning Algorithms, Self-Regulation Techniques

@booklet{EasyChair:14361,
  author    = {Oluwaseun Abiade},
  title     = {"Advancing Intelligent Systems with Neural Networks: Exploring Dynamic Systems and Computational Models for Enhanced Adaptability"},
  howpublished = {EasyChair Preprint 14361},
  year      = {EasyChair, 2024}}