Adaptive Neuro-Fuzzy Inference System (ANFIS)?

Usually in order to develop a fuzzy system you have to determine the if-then rules, suitable membership functions, and their parameters. This is not always a trivial task, especially the development of correct if-then rules may be time consuming as we first have to "extract" the expert knowledge somehow.

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Artificial-intelligence machine-learning classification link|improve this question asked Mar 5 '11 at 19:17cMinor3,3742841 99% accept rate.

Usually in order to develop a fuzzy system you have to determine the if-then rules, suitable membership functions, and their parameters. This is not always a trivial task, especially the development of correct if-then rules may be time consuming as we first have to "extract" the expert knowledge somehow. This is where ANFIS comes into play: Under certain circumstances it can automatically determine suitable parameters for the membership functions.

This is the case in particular when we already have a set of input and related output variables and values. Like in an artificial neural network the ANFIS system is able to adapt its nodes and connections between them "automatically". To your question: you could of course create an ANFIS system for your desease classification, as long as you already have input and output data for system training available.

But its not necessarily tied to such systems, you can see ANFIS more an approach usable under the mentioned circumstances, than a tool for a specific problem. It all depends on the requirements for the system you want to create, as well as the known (external) preconditions... Hope that helps!

So far I have found a university 'Monash' that explains (based on the guide of Matlabs's Fuzzy Logic Toolbox) ANFIS. The fuzzy inference system that we have considered is a model that maps: input characteristics to input membership functions input membership function to rules rules to a set of output characteristics output characteristics to output membership functions the output membership function to a single-valued output, or, a decision associated with the output.

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