Fuzzy neural networks with certainty factors

The integration of certainty factors (CFs) into the neural computing framework has resulted in a special artificial neural network known as the CFNet. This paper presents the fuz-CFNet which is devoted to classification domains where instances are described by continuous attributes. A new mathematical analysis on learning behavior, specifically linear versus nonlinear learning, is provided that can serve to explain how the fuz-CFNet discovers patterns and estimates output probabilities. Its advantages in performance and speed are demonstrated in the empirical studies