NN

II. Neural Networks

Idea: Use a three-layer perceptron network, where each layer has its own weights and bias functions, to classify data into six different postures.

Steps:

Initialize weights and bias units to small values.
Input layer uses a logsin transfer function with 56 inputs.
Hidden layer has 45 hidden units (uses a logsin transfer function) and output layer (purelin transfer function) has 6 output units.
Uses gradient descent backpropagation to find weights and bias units of each layer.
Simulate network for a test set.

.Results.

Neural Network Params
Hidden Units	24	24	40	60	50	40	45
Transfer Functions	logsig,logsig,logsig	logsig, logsig, purelin	logsig, logsig, purelin	logsig, logsig, purelin	logsig, logsig, purelin	logsig, logsig, purelin	logsig, logsig, purelin
Epochs	500	500	500	500	500	500	500
Learning Rate	0.1	0.001	0.0001	0.0001	0.00001	0.000001	0.0000001
% Recognition Train	41.50%	50.67%	61.83%	60.17%	60.00%	60.00%	61.17%
% Recognition Test	35.33%	39.67%	48.00%	47.00%	46.33%	47.33%	54.67%

Neural Network Params
Hidden Units	45	47	45	45	40	55	65
Transfer Functions	logsig, logsig, purelin	logsig, logsig, purelin	logsig, logsig, purelin	tansig, logsig, purelin	tansig, logsig, purelin	tansig, logsig, purelin	tansig, logsig, purelin
Epochs	500	500	500	700	700	700	700
Learning Rate	0.000000001	0.0000001	0.0000001	0.0000001	0.0000001	0.0000001	0.0000001
% Recognition Train	59.50%	55.50%	60.50%	61.33%	61.17%	63.67%	60.67%
% Recognition Test	46.67%	46.67%	52.00%	50.67%	46.33%	47.00%	53.00%

Neural Network Params
Hidden Units	75	100
Transfer Functions	tansig, logsig, purelin	tansig, logsig, purelin
Epochs	700	700
Learning Rate	0.0000001	0.0000001
% Recognition Train	61.83%	60.83%
% Recognition Test	50.67%	46.00%