Did we always need the 3 rules quoted below in the end result for a neural network? Could 2 or just 1 make a formidable network? I am seeing that if all 3 rules were met by a network, the number of inputs > the number of neurons in the hidden layers > the number of outputs
3 initiating rules:
The number of hidden neurons should be between the size of the input layer and the size of the output layer.
The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer.
The number of hidden neurons should be less than twice the size of the input layer.
The 3 rules above are taken from an Introduction to Neural Networks for Java (second edition) by Jeff Heaton available as a Google Book.