ONE INCREMENTAL ADAPTATION STRATEGY OF THE DECISION-MAKING FORMAL NEURON

The general objective of formal neuron adaptation is to give the reliable inputs more influence in determining the output than the unreliable inputs have. Adaptation is introduced into the threshold decision element by a circuit that performs two operations: estimates the error probability of each input and uses the estimate to change each voteweight. A cyclic error-counting adaptation procedure is the one in which vote-weights are changed periodically (cyclically), based on data collected during computations in a period (cycle). If appropriate incremental adjustments are made to the estimate of inputs' each error after each computation, then we deal with continuous adaptation. There are two methods for detecting errors: comparison with the output decision (closed-loop adaptation) and comparison with an externally supplied correct answer (open-loop adaptation). In this paper, the input weighted sum is compared with the desired value of this sum, which is multiplied by the correct binary answer. Our results for incremental changes of the input weights of the adaptive formal neuron are based on the Widrow-Hoff algorithm and stochastic approximation method.