Fuzzy neural network

A fuzzy neural network or neuro-fuzzy system is a learning machine that finds the parameters of a fuzzy system (i.e., fuzzy sets, fuzzy rules) by exploiting approximation techniques from neural networks.
Contents
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* 1 Combining fuzzy systems with neural networks
* 2 Characteristics
* 3 Cooperative Fuzzy Neural Network
* 4 Hybrid Fuzzy Neural Network
* 5 References
* 6 Recommended reading

Combining fuzzy systems with neural networks

Both neural networks and fuzzy systems have some things in common. They can be used for solving a problem (e.g. pattern recognition, regression or density estimation) if there does not exist any mathematical model of the given problem. They solely do have certain disadvantages and advantages which almost completely disappear by combining both concepts.

Neural networks can only come into play if the problem is expressed by a sufficient amount of observed examples. These observations are used to train the black box. On the one hand no prior knowledge about the problem needs to be given. On the other hand, however, it is not straightforward to extract comprehensible rules from the neural network's structure.

On the contrary, a fuzzy system demands linguistic rules instead of learning examples as prior knowledge. Furthermore the input and output variables have to be described linguistically. If the knowledge is incomplete, wrong or contradictory, then the fuzzy system must be tuned. Since there is not any formal approach for it, the tuning is performed in a heuristic way. This is usually very time consuming and error-prone.
Table 1: Comparison of neural control and fuzzy control Neural Networks Fuzzy Systems
no mathematical model necessary no mathematical model necessary
learning from scratch apriori knowledge essential
several learning algorithms not capable to learn
black-box behavior simple interpretation and implementation

It is desirable for fuzzy systems to have an automatic adaption procedure which is comparable to neural networks. As it can be seen in Table 1, combining both approaches should unite advantages and exclude disadvantages.