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Using a Boolean derivative to evaluate the significance of properties of recognized objects

This paper offers an approach for evaluating the significance of individual characteristics of recognized objects. The scope of this approach is not the subject area where objects and characteristics of these objects are specified, but a trained ∑Π - neural network that works correctly on the specified subject area. In this paper, we propose a method for constructing a crucial function based on the weight characteristics of a correctly functioning ∑Π - neuron. A logical derivative is used to evaluate the significance of object characteristics. This makes it possible to track how the decision function will change its value if one or more object characteristics change their value. This will allow us to draw a conclusion about the most important properties of the subject area under consideration.