Dynamic Object Identification using Neural Networks

Dynamic object identification plays a critical role in a wide range of engineering applications, including autonomous navigation, robotics, and adaptive control systems. Traditional identification techniques often struggle when dealing with nonlinearities, time delays, and system uncertainties inherent in dynamic environments. Recently, neural networks have emerged as a promising solution due to their ability to approximate complex nonlinear mappings and learn from noisy data in real time. This study explores the use of different neural network architectures—specifically, Feedforward Neural Networks (FNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks—for the identification of dynamic objects whose parameters change over time. Simulation experiments using both synthetic and real-world datasets demonstrate that LSTM networks provide superior accuracy and robustness, particularly in capturing temporal dependencies and sudden transitions in system behavior. The findings suggest that neural networks can serve as effective alternatives to traditional methods, enabling adaptive modeling and prediction in complex dynamic environments. This paper also discusses the implications of network architecture choice, training data quality, and hyperparameter tuning on overall system performance.