Contents
XIX
15.2Materials and Methods ........................................................................ 292
15.2.1 Data .............................................................................................. 293
15.2.2 Evolutionary Learning ................................................................. 294
15.2.3 Greedy Partitioning Algorithms ................................................... 296
15.3Results ................................................................................................ 296
15.3.1 Prediction of Chlorophyll-a ......................................................... 296
15.3.2 Prediction of Algal Species Assemblages .................................... 304
15.4Discussion ........................................................................................... 309
15.5Conclusions ......................................................................................... 309
Acknowledgements ............................................................................. 310
References ......................................................................................... 310
16. Multivariate Time-Series Prediction of Marine Zooplankton by
Artlficial Neural Networks ....................................................... 313
16.1 Introduction ......................................................................................... 313
16.2Generalisation ..................................................................................... 315
16.3Automatic Termination of Training .................................................... 318
16.4Case Study: Zooplankton Prediction ................................................... 322
16.5Conclusions ......................................................................................... 325
Acknowledgement ................................................................................. 326
References ............................................................................................. 326
17. Classification of Fish Stock-Recruitment Relationships in
Different Environmental regimes by Fuzzy Logic Combined
wlth a Bootstrap Re-sampling Approach ............................. 329
17.1 Introduction ......................................................................................... 329
17.2Fuzzy Stock-Recruitment Model ........................................................ 330
17.2.1 Traditional Stock -Recruitment Model ......................................... 330
17.2.2 Fuzzy Stock-recruitment Model .................................................. 332
17.2.2.1 Fuzzy Membership Function (FMF ............................................. 333
17.2.2.2 Fuzzy Rules ................................................................................. 334
17.2.2.3 Fuzzy Reasoning .......................................................................... 335
17.3Hybrid Optimal Leaming and Bootstrap Re-sampling Algorithms .... 336
17.3.1 Hybrid Optimal Learning Algorithms ......................................... 337
17.3.2 Bootstrap re-sampling Procedure ................................................. 339
17.4Two Real Data Analyses ..................................................................... 340
17.4.1 West Coast Vancouver Island Herring Stock .............................. 340
17.4.1.1 Data Prescription and Preliminary Analyses ............................... 340
17.4.1.2 Fuzzy-SR Model Analysis ........................................................... 342
17.4.1.3 Bootstrap Re-sampling Analysis ................................................. 344
17.4.2 Southeast Alaska Pink Salmon .................................................... 345
17.4.2.1 Data Prescription and Preliminary Analysis ................................ 345
17.4.2.2 Fuzzy-SR Model Analysis ........................................................... 346
17.4.2.3 Bootstrap Re-sampling Analysis ................................................. 347
17.5Summary and Discussion .................................................................... 347
Acknowledgements ............................................................................... 349
References ............................................................................................. 349