Unraveling the Complexity with Applications of Nonlinear Analysis in Signal Processing, Social Science, and Communication Engineering

Abstract

As a result of its power, nonlinear analysis has prevailed as a significant tool for the treatment of complex systems in signal processing, social science, and communication engineering. In this research, four key algorithms in Nonlinear methodologies are analyzed to see what is the better approach: Nonlinear Kalman Filtering, Chaos Based Neural Networks, Fractal Dimension Analysis and Nonlinear Entangled Networks. Experimental results demonstrate that nonlinear models have higher predictive accuracy and ability to detect patterns compared to traditional linear models. For example, the Nonlinear Kalman Filter was able to reduce the amount of signal noise by 18% over other filtering methods, as well as 22% improvement in classification using Chaos-Based Neural Networks. Network clustering efficiency was enhanced 15% by Fractal Dimension Analysis; and Nonlinear Entanglement Networks indicated an improvement of 20% in the detection of key nodes in complex networks. This results document the robustness and versatility of such nonlinear techniques for dynamic uncertain environments. The proposed nonlinear methods are more adaptable and computationally efficient than the studied research. Nevertheless, algorithm complexity and real-time implementation are still to be looked at. The approached in this study should be extended to improve nonlinear frameworks and hybrid models to incorporate a wider spectrum of applications. Finally, this study confirms the existence of the peculiar power of nonlinear analysis in various disciplines and leading to unprecedented advances in data driven decision making and models of systems.