Question
Asked 29th Oct, 2020

How to visualize ANFIS (Analytical Neuro-Fuzzy Inference System) network in R?

Hello!
I have successfully developed and implemented ANFIS in R with the help of FRBS package. Just one thing that is remaining is to visualize the ANFIS network.
Currently due to some constraints because of COVID, I don't have any access to Matlab while working from home. So I was wondering if there is any way to implement it in R.

Most recent answer

22nd Nov, 2021
Thomas Ntoupi
University of West Attica
how are the rules extracted, via greed partition or by using clustering method ?
also if i rerun the program, i get other results with this package, does anyone else have this problem

All Answers (6)

29th Oct, 2020
David Eugene Booth
Kent State University
It's already in R. Look in packages for MATLAB AND download. Also see:
Best, D. Booth
30th Oct, 2020
Henry Nunoo-Mensah
Kwame Nkrumah University Of Science and Technology
Checkout the full documentation at https://rdrr.io/cran/frbs/man/frbs-package.html
However, I have extracted these lines for you. Hope they help.
  • There exist functions summary.frbs and plotMF to show a summary about an frbs-object, and to plot the shapes of the membership functions.
  • Exporting an FRBS model to the frbsPMML format can be done by executing frbsPMML and write.frbsPMML. The frbsPMML format is a universal framework adopted from the Predictive Model Markup Language (PMML) format. Then, in order to consume/import the frbsPMML format to an FRBS model, we call read.frbsPMML.
1 Recommendation
30th Oct, 2020
Shibaprasad Bhattacharya
Jadavpur University
Henry Nunoo-Mensah Thank you, Sir. I will check them out.
Deleted profile
Shibaprasad Bhattacharya Could you advise whether you were able to visualise the ANFIS network in R please? Thanks
10th Mar, 2021
Shibaprasad Bhattacharya
Jadavpur University
Ritesh Pabari
No I couldn't. I used Matlab instead for ANFIS. It was quite robust. You could view the architecture, rules etc quite easily. And you could also customize membership function with ease.
1 Recommendation
22nd Nov, 2021
Thomas Ntoupi
University of West Attica
how are the rules extracted, via greed partition or by using clustering method ?
also if i rerun the program, i get other results with this package, does anyone else have this problem

Similar questions and discussions

How to interpret generalized additive model (GAM) summary of statistics in R?
Question
5 answers
  • Abraham EustaceAbraham Eustace
I have run a GAM model and got summary of statisticts (see below and attached pdf). I'm aware that, the parametric coefficients are interpreted just like a normal GLM however I'm not clear on how to interpret the approximate significance of smooth terms. Please, if anyone is aware of this, I need your help.
Formula:
Abundance ~ Vegetation + s(dist_road_km) + s(dist_boundary_km) +
s(dist_waterway_km) + te(dist_waterway_km, dist_road_km) +
Vegetation:dist_boundary_km + Vegetation:dist_waterway_km
Parametric coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.000 0.000 NA NA
VegetationWooded grassland -171.897 39.408 -4.362 1.29e-05 ***
VegetationGrassland -167.544 39.396 -4.253 2.11e-05 ***
VegetationRiverline forest:dist_boundary_km 34.162 7.644 4.469 7.85e-06 ***
VegetationWooded grassland:dist_boundary_km 67.767 15.426 4.393 1.12e-05 ***
VegetationGrassland:dist_boundary_km 65.308 15.202 4.296 1.74e-05 ***
VegetationRiverline forest:dist_waterway_km 55.308 16.177 3.419 0.000629 ***
VegetationWooded grassland:dist_waterway_km 0.000 0.000 NA NA
VegetationGrassland:dist_waterway_km 2.915 1.193 2.445 0.014503 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df Chi.sq p-value
s(dist_road_km) 8.999 8.999 61.01 9.39e-10 ***
s(dist_boundary_km) 8.931 8.994 103.16 < 2e-16 ***
s(dist_waterway_km) 8.987 8.999 60.69 9.65e-10 ***
te(dist_waterway_km,dist_road_km) 20.151 20.620 135.81 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Rank: 56/58
R-sq.(adj) = 0.556 Deviance explained = 87.8%
UBRE = 1.4389 Scale est. = 1 n = 73

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