A Novel Fuzzy-Neural Slack-Diversifying Rule Based on Soft Computing Applications for Job Dispatching in a Wafer Fabrication Factory

Algorithm 2

The BPN code in MATLAB (category 1).

tn_input  =    [0.500  0.300  0.500…;  0.396  0.574  0.811…;  0.900  0.900  0.900…;  0.722  

  0.811  0.900…;  0.700  0.567  0.633…;  0.279  0.314  0.314…]

tn_target  =  [0.341  0.383  0.598⋯ 0.100]

net    =    newff([0  1;  0  1;  0  1;  0  1;  0  1;  0  1],[12,  1],{“logsig”,  “logsig”},  “trainlm”);

net    =    init(net);

net.trainParam.show  =  10;

net.trainParam.lr  =  0.1;

net.trainParam.epochs  =  1000;

net.trainParam.goal  =  1e  −  4;

[net,  tr]  =  train(net,  tn_input,  tn_target);

tn_output  =  sim(net,  tn_input)

te_input  =  [0.300  0.500  0.300…;  0.307  0.396  0.870…;  0.900  0.900  0.900…;  

0.722  0.811  0.900…;  0.767  0.700  0.900…;  0.158  0.264  0.302…]

te_output  =  sim(net,  te_input)