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The second key parameter to this learning rule involves the initial variance of the random distribution of the weights. In most of the commercial packages there is a vendor recommended number for this initial variance parameter. Yet, the setting of this number is not all that important as the self-adjusting feature of the directed random search has proven to be robust over a wide range of initial variances. There are four key components to a random search network. They are the random step, the reversal step, a directed component, and a self-adjusting variance.

Hebb's Rule: The first, and undoubtedly the best known, learning rule was introduced by Donald Hebb. The description appeared in his book T h e Organization of Behavior in 1949. His basic rule is: If a neuron receives an input from another neuron, and if both are highly active (mathematically have the same sign), the weight between the neurons should be strengthened. Hopfield Law: It is similar to Hebb's rule with the exception that it specifies the magnitude of the strengthening or weakening. " The Delta Rule: This rule is a further variation of Hebb's Rule.

This rule is commonly used, even though it converges to a point of stability very slowly. It has been shown that different learning rates for different layers of a network help the learning process converge faster. In these tests, the learning rates for those layers close to the output were set lower than those layers near the input. This is especially important for applications where the input data is not derived from a strong underlying model. Kohonen's Learning Law: This procedure, developed by Teuvo Kohonen, was inspired by learning in biological systems.

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