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-10, 20 or 40 neurons in the hidden layer

-a bias input was connected to each neuron

-the layers were all fully connected

-there were runs with one, and two hidden layers

16.4.1.4 - The Training Set

•The problem is reduced using either left or right arm configurations, the solution is also constrained to elbow up or elbow down.

•Discontinuities were avoided by not training the neural network in the region above the origin. The elbow straight configuration is also a minor singularity problem.

•Training points were evenly distributed throughout the robot workspace

•Only a quarter of the robot workspace was used because of the robot symettry.

•The general protocol for training was,

-apply the desired position to the input, and train for the desired joint angles.

-When accuracy was high enough, the first correction net was trained by comparing the actual errors, and the desired values. Additional correction networks were also trained in some cases.

-the error was measured by using an RMS measure of the differences

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•As seen in the experimental results, there are distortions that occur near the origin, and the edges of the workspace, as would be expected with the singularities found there.

•The errors also increased near the training boundaries

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16.4.1.5 - Results

•The mathematical sigularities caused by cartesian coordinates, and the +/- 180 degrees singularity could be eliminated by selecting another set of coordinates for space and the arm.

•The best results were about 1 degree RMS.

•