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Sep 25, 2007, 12:53 PM
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Neural Networks Triumph Again


Neural network assisted hover 4 U:

Neural network autonomous hover (1 min 0 sec)


Even with the 10m error, keeping her in frame for the entire flights was impossible.

Well, the neural network is definitely a different world than last month. A few months ago we were ecstatic with 20 seconds of autopilot. Now position hold is good enough that we don't even look at her at all times anymore. On a global scale, she can hold position. On an American scale, the open spaces are a bit smaller and the position hold is lousy but positive.

The last 2 flights were in a 5mph offshore wind. On flight 1, she sayed within 10m of the target position for 4 minutes. On flight 2, after staying within 10m of the target position for 3 minutes, for some reason she headed off at constant velocity. Getting rid of position error integrals seemed to fix yesterday's oscillation.

Altitude hold was handed back to the computer. Had to manually trim the throttle periodically as the battery drained and that may ruin the neural network output. On a full battery it works. A rotor tachometer for altitude hold is looking better & better.

The neural network can't predict climb rate from throttle at all but we have it predicting climb rate anyway because it feels like it's working. Lack of correlation between head speed & throttle seems 2 B the main problem with this.

With in-flight training + Kalman navigation, the Gumstix can only do around 25 iterations per GPS update. In flight training greatly improves the prediction but it takes over 1000 iterations per GPS update. The only solution may be an FPGA. With the offline training, iterations over 1000000 and hidden nodes over 15 seem 2 make no difference. May be a limitation in lwneuralnet since that uses a lookup table.

Until next week, the next step is to try evolutionary training, switching back to algebraic navigation, enabling a second neural network for feedback.
Last edited by Jack Crossfire; Sep 25, 2007 at 03:05 PM.
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