Catalogue of Artificial Intelligence Techniques
Aliases: fuzzy neural networks, neuro-fuzzy systems
Keywords: fuzzy, learning, network, neural, neuro-fuzzy, tsk
Categories: Neural Networks
Author(s): Konstantinos Doxiadis
Neuro-fuzzy hybridization is a modern Artificial Intelligence technique also known as "fuzzy neural networks" or "neuro-fuzzy systems". This technique merges two different fields of artificial intelligence, fuzzy logic and artificial neural networks, combining positive attributes form both approaches (i.e. a hybrid intelligent system). This result to what can be viewed as a three-layer artificial neural network with input variables, fuzzy rules and output variables representing the three layers. Fuzzy neural networks are ideal for creating models that can adapt and respond to real-world environmental stimuli. This is based on the principle that they combine a human-like reasoning of fuzzy systems in combination with a learning algorithm inspired from the Neural Network theory. They are ideal for a wide range of applications such as real-time dynamic processing, pattern recognition and even linguistics.
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