Mengenal ANFIS (Adaptive Neuro Fuzzy)
Adaptive Neuro Fuzzy Inference System (ANFIS) saat ini ramai digunakan sebagai sistem pengontrol atau estimator/predictor. Program yang dikembangkan dari kode demo ANFIS dari Roger Jang dapat didownload di toko-elektronika.com pada bagian download kode buku Kendali Cerdas.
As we have already seen, fuzzy systems present particular problems to a developer:
Rules. The if-then rules have to be determined somehow. This is usually done by ‘knowledge acquisition’ from an expert. It is a time consuming process that is fraught with problems.
Membership functions. A fuzzy set is fully determined by its membership function. This has to be determined. If it’s gaussian then what are the parameters?
The ANFIS approach learns the rules and membership functions from data.
ANFIS is an adaptive network. An adaptive network is network of nodes and directional links. Associated with the network is a learning rule – for example back propagation. It’s called adaptive because some, or all, of the nodes have parameters which affect the output of the node. These networks are learning a relationship between inputs and outputs.
Adaptive networks covers a number of different approaches but for our purposes we will investigate in some detail the method proposed by Jang known as ANFIS.
The ANFIS architecture is shown below. The circular nodes represent nodes that are fixed whereas the square nodes are nodes that have parameters to be learnt.
An ANFIS architecture for a two rule Sugeno system
For the training of the network, there is a forward pass and a backward pass. We now look at each layer in turn for the forward pass. The forward pass propagates the input vector through the network layer by layer. In the backward pass, the error is sent back through the network in a similar manner to backpropagation.
Berikut hasil program yang dibuat :
Program di atas dibuat berdasarkan referensi :
[1] “ANFIS: Adaptive-Network-based Fuzzy Inference Systems,” IEEE Trans. on Systems, Man and Cybernetics, Vol. 23, No. 3, pp 665-685, May 1993.
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