Skip to main content

Theory and Modern Applications

Table 7 Unknown parameters in the LeNN structure obtained by the proposed algorithm for optimization of different cases of problem 1

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

−0.073712

  

0.809108

  

−0.273014

  

−0.0619974

  

2

−0.123811

−0.954885

−0.985387

0.715874

−0.994894

−0.025614

0.562050

0.8684705

1.0063691

−0.6762624

−0.4741334

−0.6304957

3

0.688482

−0.001984

0.8147776

−0.608978

0.755628

−0.578253

0.990777

−0.9010228

−0.4239812

−1.1455986

1.0669554

−0.3098253

4

0.607721

−0.999805

0.643845

0.990423

0.011483

0.283251

−0.242936

0.9658161

0.6908204

−0.2412426

−0.4037276

−0.7738522

5

−0.252155

−0.277830

0.204102

0.991523

0.496766

−0.738337

0.523024

0.0749905

0.3770240

−1.0253661

0.3443633

0.1349037

6

−0.999841

0.390553

−0.437502

0.838626

0.131377

0.262646

0.771747

0.6053185

−0.3784280

0.1722285

0.7451329

−0.2318408

7

−0.364695

−0.094448

0.831538

0.994616

0.408626

−0.029665

1.120715

−0.1839236

0.6611971

0.3259415

−0.4048159

0.9430688

8

0.360412

−0.308318

−0.180185

0.988844

0.173220

0.495972

1.007335

−0.1582548

0.9502834

−0.7829180

0.4562725

−0.3961226

9

0.415674

0.216478

−0.064584

0.357771

−0.508501

0.201958

0.933068

−0.1727297

0.2428355

1.3394897

−0.0663415

0.8727372

10

0.118284

0.083259

−0.369873

−0.972692

0.222064

0.182369

1.205046

0.3057650

0.1062000

−0.6916889

−0.2538331

0.3934666

11

−0.223678

−0.042991

0.230265

−0.057418

0.527491

−0.480111

−7.68E − 05

−0.8863328

0.1069155

−0.2624651

0.4643374

−0.2097343

12

−0.636076

0.194459

0.001038

0.013523

−0.516267

−0.093195

0.001813

0.7883565

−0.581578

0.0001646

1.1266980

−0.6623952