Example applications of neural networks for MIMO system control and modeling

Abstract

The target of this thesis is to focus on the application of Neural Networks (NNs) in Multiple Input Multiple Output (MIMO) control systems and take a literature review how these are applied in different applications. The main idea is to highlight the effectiveness and flexibility of neural networks in dealing with complex system control problems by showing some of the recent examples of neural network control applied in different MIMO systems. In particular, the thesis focuses on how the learning capabilities of neural networks can be exploited to optimize and adaptively manage the performance of MIMO systems, thus overcoming the challenges faced by conventional control strategies. This is done by analyzing different types of neural network control methods such as back propagation neural networks (BPNN), convolutional neural networks (CNN), and recurrent neural networks (RNN), and their application cases in various fields such as underwater glider control, wireless sensor network cluster head identification, nutrient conductivity control system, and indoor localization. These cases demonstrate the potential of neural networks in improving the control accuracy and robustness of complex MIMO systems and also demonstrate that combining neural networks with traditional control techniques can result in more effective and adaptable control solutions.