Create a Fuzzy Expert System with the following specification:
This is an often used example: The problem the expert system should address is the supplemental feeding of the range cows on the basis of the energy and protein levels of current range forage.
The input linguistic variables:
The output linguistic variable:
The Fuzzy rules:
Rule 1: if protein is low and energy is low then feed is high
Rule 2: if protein is low and energy is medium then feed is medium
Rule 3: if protein is medium and energy is low then feed is medium
Rule 4: if protein is high and energy is high then feed is low
You are free to choose the range and the membership functions for the linguistic terms for the input and output linguistic variables. That is the terms: “Low”, “medium”, and “high” for protein, “low” “medium”, and “high” for the energy level, and “Low”, “medium”, and “ high” for the feeding amount .Experiment with different ranges and membership functions to see the effect. Make sure that the Rules viewer shows the correct working according to the above supplied rules.
Save the final fuzzy inference system (fuzzy expert system) as cowfeeding.fis
Create fuzz expert system for the service center case study covered in lecture 7. Use the Rule base 1 with 12 rules ( shown in slide 13-Uploaded PPT attachment). Save the final fuzzy inference system (fuzzy expert system) as service_center.fis