Yes, PATTERNNET is recommended for classification problems.
TRAINLM is a good training function for most problems. For small dataset problems TRAINBR may produce better generalization.
There is no need to worry about the number of epochs or learning rate or other details. These have good default values and training stops automatically when the optimization gradient becomes small enough or generalization is optimized (by validation with TRAINLM or regularization with TRAINBR).
You can train a few networks, as each time TRAIN is called different initial weights and biases are used and the data is divided differently for training, validation and test sets. Then choose the network that generalizes best to new data.
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