Specifications Yes No pretrained: X Linear Logarithmic Differentiated Inputs: X Mean Std Dev Modify distribution Normalization: X X sigmoid tanh arctan Transition fnct: X Two Three Four Five Levels: X X X Max neurons per level: 256 Bias (reference input): Yes No X seed: random reseed: user reset button verification: intuitive/graphical ---------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------------------- Notes The two most important choices to make in developing this network are the transition function and the learning method. For GoldenGem these choices were based math and common sense, but they are also very well justified in the literature. See points 1. and 2. below. 1. Our choice to use the arctan transition function is justified by many sources, for instance comp.ai.neural nets "The arctan function is usually better than the tanh function" 2. Our choice to use the gradient (backprop) method is justified by many sources, for instance Karl Nygren, in a recent Masters thesis at Royal Inst of Technology in Sweden describes it as "unchallenged as the most influential learning algorithm for multilayer perception." This masters thesis goes on (as most scholarly articles do) to suggest a minor improvement to the algorithm, which is disjoint from improvements suggested elsewhere. 3.Since the inputs are normalized to mean zero, a bias neuron is neeeded to break the symmetry in layer one. The Subsequent layers do not need a bias neuron. 4. Adjustable sensitivity is crucial to the functioning of the neural network. Human cognition must guide the essentially unintelligent neural network (which however has greater accuracy and capacity for numerical calculation than the unaided human mind). 5. User choice of a set of related tickers also gives crucial capacity for user interaction. 6. Our choice not to include filtering, or any successive difference inputs, was a hard choice; this is justified by the fact that filtering loses some of the most recent information, causes a time delay. If we were to include successive differences, we should need to make a choice of how far back in time to take the second (negative) input. The result, by the way,would precisely be a rudimentary digital filter. One of the two data points would become unavailable before the last data point had been reached, and so the last valid data point would be in the past, rather than the current day. To conclude this brief list of notes -- the most natural choice of a neural network configuration from elementary mathematical considerations, is precisely the same configuration which the artificial intelligence community has settled on as the basic standard.Home Page