Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...
Abstract: This paper introduces a control framework that leverages Lagrangian neural networks (LNNs) for computed torque control (CTC) of robotic systems with unknown dynamics. Unlike prior LNN-based ...
Figure 1. Neural networks can store and recall information. In a recall task where the desired output pattern is identical to the input pattern, several patterns can ...
Learn how forward propagation works in neural networks using Python! This tutorial explains the process of passing inputs through layers, calculating activations, and preparing data for ...
AI systems learn patterns from data rather than following explicit instructions. Neural networks process information through connected layers to detect complex patterns. Modern AI chatbots like ...
Abstract: Due to the fact that the Polyvinyl Chloride (PVC) polymerization process is a time-varying, nonlinear process with large time delays, there are many uncertainties and external disturbances ...
Deep learning has added a new dimension to engineering applications, from 5G signal processing to predictive maintenance in power grids. It automatically detects equipment failures and optimizes ...
AI transforms RF engineering through neural networks that predict signal behavior and interference patterns, enabling proactive system optimization and enhanced performance. The convergence of machine ...
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