Neural Networks Sarah Ezzell Engr. 315

What Are They? Information processing system (non- algorithmic, non-digital) Inspired by the human brain Made of artificial neurons (neurodes) – Crude approximation of biological neurons – Connected by weights over which signals travel – Arranged in layers

Neural Network Diagram Input Layer Middle Layer Output Layer Input Signals Output Signals Neurode Connection Link

Translation – Input Stimuli to Output Response 3 steps – Neurode computes net weighted input received – Converts net input into an activation level – Converts activation level into output signal

Learning Capability Learns to solve problems, not just programmed Learning achieved by modifying weights Training methods – Supervised – Graded – Unsupervised

Control System Applications Aircraft Manufacturing plant Power plant

Aircraft – Flight Control System Neural network like a human, but better – Adapts like human, only quicker and more accurately Back propagation vs. on line learning Network integrated with current flight control systems – Removes “gain scheduling”

Manufacturing Plant – Hot Dip Galvanized Steel Strip Manually controlled coating thickness controls Neural network control model – Developed by Siemens and Thyssen Stahl – Cost-effective, better quality product – On line learning Eliminates need for hot measuring equipment

Power Plant – Utility Boilers Air pollutants from power plants NeuSIGHT system – Used in coal-fired electric power plants – Provides real time closed-loop supervisory control – Improves operating efficiency & reduces emissions