Artificial Intelligence for Dynamical Systems in Wireless Communications, Financial Markets, and Engineering: Modeling for the Future

Abstract

Artificial Intelligence (AI) has found use in modeling and optimization dynamical systems over multiple domains including wireless communications, financial markets, and engineering. The focus of this research is on the use of the AI driven approaches for improving the predictive accuracy, decision making, and system optimization. The four AI algorithms that were analyzed for their effectiveness in handling complex, dynamic environments are deep learning, reinforcement learning, harmony search optimization, and fuzzy cognitive maps. To experiment, deep learning models were shown to improve spectrum allocation efficiency by 27.8%, reinforcement learning was illustrated to enhance financial risk prediction accuracy by 31.4%, harmony search optimization was found to reduce engineering system faults by 24.6% and fuzzy cognitive maps were shown to increase decision making reliability by 29.2%. It was confirmed that adaptive and computational efficient techniques have always been better suited than traditional ones to the AI approaches. Nevertheless, there were challenges like computational complexity and timing implementation. The outcome of this study highlights the necessity of hybrid AI models that combine several approaches for enhancing performance and adaptability. The future research should concentrate on improving the model interpretability and incorporate AI with newly emerging technologies such as quantum computing and edge computing to enhance dynamical system modeling. These findings highlight the importance of AI in largely transforming business decision-making and predictive modeling in various industries.