Systems can start with random weights, input training pattern, compare output to input, slightly reduce weights on units that are too high and slightly increase weights on units that are too low, and repeat {backpropagation, connectionism} {backward error propagation}. For example, after neural networks have processed input and sent output, teacher circuits signal node differences from expected values and correct weighting. System performs process again. As process repeats, total error decreases.
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Date Modified: 2022.0224