Adaptive Neuro-Fuzzy Inference System (ANFIS)

Do you have an example or an explanation of ANFIS (Adaptive Neuro-Fuzzy Inference System), I am reading that this could be applied to classify some diseases, What do you think开发者_开发问答 about it?

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!

As Matthias said ANFIS is not mapped to a particular problem, you can use it on the basis of problem requirement. But where to use ANFIS: You can use it with any problem where something is ambiguous.

Actually this is the property of FIS(Fuzzy Inference System). Adaptive come in role as Matthias explained.

For ex. took famous classification problem, classifying a input to any class is not always perfectly determined, it somewhat ambiguous. So there using ANFIS may give better results then other classification algorithms depending upon whether you are able to model the system correctly or not using ANFIS.

But using ANFIS is computationally expensive as compared to other non-fuzzy approches. As to make FIS to perfect model your problem you will add AN part to it. This only make membership function selection adaptive. What about if-then rules. For that you have to do unsupervised rule selection from the complete possible rule base(this is basically a kind of unsupervised clustering problem, where you are trying to group all the rules whose effect would be same).

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.

Yes it can be used for Diseases Classification. Since the idea of ANFIS is combine fuzzy system in architecture of ANN. In this case, ANFIS have two main benefit.

first, you can use fuzzy variable which is support for Linguistic variable and it's fit for Diseases's symptoms that are commonly used as system's input (example of input >> pain levels : low, mid, high).

Second, since the architecture is mapped to ANN layers, ANFIS can do training process which aims to create more accurate result (ex : use Backpropagation method).