<p> Lecture 19 Neural Networks 1. Description of Neural network elements from Simpson a. Comparison with synaptic action of nerve interconnections b. Focus on the non-linearity of the threshold function c. Do XOR problem with 3-layered network. Show that it only works for “Step-wise” activation function: 0 for input < 0; 1 for input > 0 d. Discuss “back-propagation” networks: Good because Sigmoid activation function enables “closed form” representation of the error gradients.</p><p>Neural network example: Driver - Automobile/Highway System</p><p>Partnership of Human Machine Interaction that provides: ubiquity of movement time and space</p><p>Current Division of Responsibility Driver - Decisions (Brains / Intelligence) Auto/Highway - Brawn (Carries out decisions)</p><p>Is this the best division of responsibility? Can the Auto/Highway do more?</p><p>Kinds of Decisions made in driving:  Macro Scale  When to leave  What route</p><p> Mid Scale  How to steer  When to shift  How hard to apply brakes  Pedal to the Metal??</p><p>01/09/18 Psy/Orf 322 Human Machine Interaction – Spring 2004  Micro Scale  When & How Much fuel to place in combustion chamber  “ “ “ air “ “ “ “  When to fire the spark plugs</p><p>Some things already done by car:  Ignition system  Headway monitoring  Automatic transmission (Smart Cruise)  Signal seeking radio  Anti-lock brakes  Cruise control  In-Voluntary  Voluntary  Navigation</p><p>What is Next???? Lane departure warning; Automated Steering???</p><p>- You mean the “flanged wheel”!?</p><p>Other Approaches???</p><p>Open-Loop Trajectories Differential GPS (30 cm accuracy) – road/lane match, UofM Snow Plows Buried Cables Machine Vision:</p><p>“Engineering Based” “Cognitive Based” Obtain parametric representation of the Use image as input to neural upcoming road; then network that is trained to Describe Vehicle Dynamics, provide desired steering Control System response and output. Objective Functions: ThenSolve: 2-Point Boundary Value Problem </p><p>01/09/18 Psy/Orf 322 Human Machine Interaction – Spring 2004</p>