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Theory and Modern Applications

Table 11 Unknown parameters in the LeNN structure obtained by the proposed algorithm for optimization of different cases of problem 2

From: Application of Legendre polynomials based neural networks for the analysis of heat and mass transfer of a non-Newtonian fluid in a porous channel

 

Case I

Case II

Case III

Case IV

 

Ï•

ω

β

Ï•

ω

β

Ï•

ω

β

Ï•

ω

β

1

0.647559

  

0.618122

  

0.322975

  

0.604064

  

2

0.907212

−0.267991

0.322864

0.236205

−0.410823

0.050089

0.125545

0.092395

−0.099459

0.145308

0.048916

0.390702

3

−0.239339

−0.067396

0.513909

−0.534078

0.671632

0.245072

−0.142836

−0.499641

0.014703

0.001974

−0.860052

0.947150

4

−0.957706

−0.124556

−0.001295

0.802993

−0.432387

0.224744

−0.422683

−0.681028

0.037512

1.004635

−0.500737

0.843710

5

0.999999

0.068524

0.198613

−0.860333

−0.356714

0.288286

−0.208811

0.548146

−0.741997

−0.027133

−0.050194

0.839738

6

0.991093

−0.300834

−0.185112

−0.877682

0.069086

−0.328927

−0.110183

0.724411

0.045469

0.046072

−0.705402

0.044515

7

−0.895437

−0.189009

−0.098513

0.997667

0.203825

0.639051

0.087693

0.735871

−0.082264

−0.050320

0.220688

−0.704819

8

−0.952240

−0.070378

−0.093129

−0.042320

0.377492

0.499963

−0.076128

−0.762465

0.218879

0.636024

0.111035

0.001623

9

−0.808541

0.064314

−0.138659

−0.177386

−0.154882

−0.525750

0.501793

0.093607

−0.998551

−0.294732

−0.707771

0.326603

10

0.929781

0.047806

−0.295206

−0.157372

−0.183808

−0.332534

0.081314

0.323525

0.327212

0.034618

−0.157209

0.456327

11

0.020025

0.464669

−0.178734

−0.709330

−0.082084

−0.032530

0.183672

−0.243237

−0.262075

0.907357

−0.081191

0.154746

12

0.978038

−0.132835

−0.091676

0.361373

0.315329

0.035493

0.045139

−0.698989

0.375621

0.178138

0.643929

−0.528214