NEURAL NETWORK AND ITS APPLICATION IN ENGINEERING IN NIGERIA

Abstract

The paper examined the development and application of neural networks in engineering practice in Nigeria, emphasizing their role in intelligent control and automation. The objectives are to analyze how neural networks can model complex systems, improve predictive accuracy, and enhance decision-making across engineering disciplines. A descriptive analytical method was used, reviewing established neural network architectures such as feed-forward and recurrent models, their learning algorithms, and their implementation in real-world engineering systems. Findings show that neural networks effectively address problems involving non linearity, uncertainty, and data complexity. In electrical and power engineering, they support load forecasting, fault detection, and renewable energy optimization. In civil, mechanical, and petroleum engineering, neural models are applied to material prediction, process optimization, and reservoir simulation, respectively. They also facilitate intelligent traffic management and environmental monitoring within Nigeria's developing infrastructure. The study concluded that neural networks offer a powerful computational framework for engineering innovation, capable of self-learning and adaptation to complex environments. Despite challenges in scalability, verification, and interpretability, their continued advancement positions them as essential tools for future engineering systems and sustainable technological growth in Nigeria.