Example #1
0
        private void Process()
        {
            bool isSuccess = false;

            while (!isSuccess)
            {
                int    max = FuzzyLogicUtil.FindMax(ChoiceValues.ToArray());
                double normOfSimilarity = 0;

                if (max != -1)
                {
                    normOfSimilarity = F2.Neurons(max).Output();
                }
                else
                {
                    // Indicate that there is no eligible neuron
                    // as winner, so create a dummy neuron in F2 layer.
                    max = F2.AddNeuron(FuzzyLogicUtil.MakeDummyNeuron(InputSize));
                    ChoiceValues.Add(
                        FuzzyLogicUtil.ChoicingValue(normOfInputs, 1.0 * InputSize, Choice));
                    normOfSimilarity = normOfInputs;
                }

                // Calculate and compare with vigilance
                winningVigilance = normOfSimilarity / normOfInputs;
                if (winningVigilance >= Vigilance)
                {
                    winningNeuronPos = max;
                    isSuccess        = true;
                }
                else
                {
                    ChoiceValues[max] = 0;
                }
            }

            // Adjust neuron weight automatically. if it is set.
            if (AutoAdjustWeight)
            {
                AdjustWeight();
            }

            // Calculate output.
            output = FuzzyLogicUtil.Intersect(F2.Neurons(WinningNeuronPos).Weights.ToArray(),
                                              InputData, InputSize);
        }
Example #2
0
        public void Run(double[] data)
        {
            input = data;
            // Compute norm of input values.
            normOfInputs = FuzzyLogicUtil.Norm(data);

            // Transfer input values.
            f1.Input(data);

            // Calculate initial choicing value
            ChoiceValues.Clear();
            for (int i = 0; i < f2.Count; ++i)
            {
                double normOfWeight = FuzzyLogicUtil.Norm(F2.Neurons(i).Weights.ToArray());
                ChoiceValues.Add(FuzzyLogicUtil.ChoicingValue(F2.Neurons(i).Output(), normOfWeight, Choice));
            }

            Process();
        }