Optimizing Communication, Financial, and IT Systems with Applied Nonlinear Analysis Techniques

Abstract

Modern communication networks, financial markets, and IT infrastructure become more complex, and traditional linear models do not provide enough power for effective optimization. Specifically, this research investigates the potential application of nonlinear analysis techniques to increase efficiency, security and predictability in all these domains. Two highly related topics were then explored: Kalman filtering for noise reduction in communication systems, fractal market analysis for financial forecasting, and chaos based encryption for IT security, and nonlinear support vector machines (SVMs) for anomaly detection. Experimental results indicated that Kalman filtering increases signal accuracy by 27%, fractal analysis enhances financial risk prediction accuracy by 21%, chaos-based encryption enhances cybersecurity resilience by 34% and nonlinear SVMs increase the rates of anomaly detection by 29% compared to naive approaches. The performance of traditional linear techniques was compared with that of nonlinear models and results confirmed that nonlinear models outperformed traditional linear techniques in the cases of dynamic and uncertain environment. While this, however, fails to resolve the issue of computational complexity, hybrid models combining deep learning and optimization frameworks are suggested. Through this research, valuable insights are introduced into employing nonlinear methods for enhancing adaptability and robustness of communication, financial, and IT systems. Real time implementation and scalability to these domains are left for future studies.