Thanks for your feedback on my issue. I am currently using evolutionary processes to tune the knowledge base of my controllers so its FLL may be quite different from the standard jfuzzylite FLL. However, I have modified the demo ObstacleAvoidance FLL according to the basic framework of all my controllers and I am certain if that will help in achieving the goal of making the fuzzy outputs probabilities then I can do same in my project. My plan-b is to take the softmax of the outputs but I was thinking the values could be probs so I do not have to. Thanks.
#File: ObstacleAvoidance.fll
Engine: ObstacleAvoidance
InputVariable: obstacle1
enabled: true
range: 0.000 1.000
lock-range: false
term: left Triangle -1.000 0.000 1.000
term: right Triangle 0.000 1.000 2.000
InputVariable: obstacle2
enabled: true
range: 0.000 1.000
lock-range: false
term: left Triangle -1.000 0.000 1.000
term: right Triangle 0.000 1.000 2.000
InputVariable: obstacle3
enabled: true
range: 0.000 1.000
lock-range: false
term: left Triangle -1.000 0.000 1.000
term: right Triangle 0.000 1.000 2.000
OutputVariable: myOutput
enabled: true
range: 0.000 1.000
lock-range: false
aggregation: Maximum
defuzzifier: MeanOfMaximum 100
default: nan
lock-previous: false
term: outputTerm1 Triangle -1.000 0.000 1.000
term: outputTerm2 Triangle 0.000 1.000 2.000
RuleBlock: mamdani
enabled: true
conjunction: Minimum
disjunction: Maximum
implication: Minimum
activation: General
rule: if obstacle1 is left and obstacle2 is left and obstacle3 is left then myOutput is outputTerm1
rule: if obstacle1 is left and obstacle2 is left and obstacle3 is right then myOutput is outputTerm1
rule: if obstacle1 is left and obstacle2 is right and obstacle3 is left then myOutput is outputTerm2
rule: if obstacle1 is right and obstacle2 is left and obstacle3 is right then myOutput is outputTerm2
rule: if obstacle1 is right and obstacle2 is left and obstacle3 is left then myOutput is outputTerm1