This process is called fuzzy inference or defuzzification . There are many techniques for defuzzifying, but the most common approach is centroid defuzzification, where the center of mass of the combined partial triangle is computed and translated on the output axis. A fuzzy system that can learn from experiences and develop its fuzzy rules based on its own experiences represents an adaptive system. This is where neural networks come in. A neural network is a system that emulates the cognitive abilities of the brain by establishing recognition of particular inputs and producing the appropriate output.
Bayesian networks resemble neural networks in their causal nature and variable dependency.
The probabilities in a Bayesian network are roughly analogous to the internal weights in a neural network. Neural networks, however, are free to form their own internal workings and adapt on their own. A fuzzy system can use a neural network to adapt to the desired outputs of the end user or changing operating conditions. For obvious reasons, this type of system is sometimes called a neuro-fuzzy system. Proper implementation of neuro-fuzzy systems can produce stunning accuracy.