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Table 1 The results of experimental test in investigating capability of NEFPROX in learning structure of a FIS

From: Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning

Test No. SE SN NExist NPartial NNew Error
1 8 8 7 1 0 4.2%
2 7 8 7 1 0 3.4%
3 6 8 5 1 2 6.6%
4 5 8 4 1 3 5.7%
5 4 8 2 1 5 4.4%
6 8 8 7 1 0 6.8%
7 8 7 7 0 0 6.6%
8 8 6 6 0 0 10.2%
9 8 5 5 0 0 10.5%
10 8 4 4 0 0 10.1%
11 8 3 3 0 0 11.6%
12 8 2 2 0 0 10.8%
13 8 1 1 0 0 10.1%
Mean       7.77 ± 0.02%
  1. SE: The size of the rules used in the existing FIS.
  2. SN: The size of the rules used in the new FIS.
  3. NExist: The number of the existing rules in the new FIS and the existing FIS.
  4. NPartial: The number of the partially-existing rules in the new FIS and the existing FIS.
  5. NNew: The number of the non-existing rules in the new FIS and the existing FIS.
  6. Error: Percentual difference between output vectors of original (manually created FIS) and trained FIS (ANFIS) for a given (identical) set of input vectors, thus providing an estimate of the "similarity" of the behaviour of the manually created oFIS and the new FIS (ANFIS) trained by the original FIS