Algorithm 1: Fuzzy Weighted Rule

1.

Take the numerical data in tabular form as the input, where the size of each record is N and number of output types are n

2.

Take M number of MFs for each of N input variable

3.

Convert numerical input data of the table into Fuzzy linguistic values using MFs

4.

Take union of Fuzzy linguistic value of 1^st field, 2^nd field, 3^rd field, …, N^th field for the case of first output

5.

If the sets obtained from the unions of step 4 are: {S₁}, {S₂}, {S₃}, …, {S_N} then the N-tuple (({S₁}, {S₂}, {S₃}, …, {S_N}), First output) known as R_{First_output}

6.

Repeat step 4 and 5 to get R_{Second_output}, R_{Third_output}, …, R_{Nth_output}

7.

8.

Repeat step 7 for 2^nd, 3^rd, …, N^th elements of R_{First_output}, R_{Second_output}, R_{Third_output}, …, R_{Nth_output} to get the rule R₂, R₃, R₄, …, R_N

9.

Take the sum of non-overlapping range and full range of first input variable against all the n output

10.

Take the ratio v₁ two terms of step 9

11.

Repeat step 9 and 10 for the rest of input variables

12.

Take $\max \left(v_1,v_2,v_3,\cdots ,v_N\right)$ and weights, $W_i=\frac{V_i}{\max \left(V_1,V_2,\cdots ,V_N\right)}$ , where

$i=\text{1},\text{2},\text{3},\cdots ,N$ (1)

13.

For each input record of N-tuple determine weighted co-variance of each rule like,

$R={\displaystyle {\sum }_{i=1}^N{\Psi }_{i,j}\left(X_j\right)W_j}$ ; (2)

where X_j is jth the input Fuzzy variable, i for ith rule, ${\Psi }_{i,j}\left(X_j\right)=1$ if X_j belongs to jth set of ith rule R_i, otherwise ${\Psi }_{i,j}\left(X_j\right)=0$

14.

The highest value of R corresponding to kth rule indicates the input tuple is under the output of kth category