Chapter 17 . Classification of Fish Stock-Recruitment Relationships 349
fuzzy sets with an associated degree of membership function based on Takagi and
Sugeno (1983). The inherent uncertainties in the environmental data were taken
into account by the fuzzification process. The Fuzzy-SR model is capable of
empirically approximating the underlying SR relationship, and can also provide a
crisp and simple functional relationship among the inputs and output according to
the fuzzy rules (two in this application). An important feature of the Fuzzy-SR
model is that the functional SR relationships described by the fuzzy rules can be
chosen to more realistically describe the biological processes that affect
recruitment.
Accordingly, the Fuzzy-SR model with the bootstrap resampling algorithm can
be a useful tool for stock recruitment analysis to fish population.
Acknowledgements
I sincerely thank Jim Irvine, Jake Schweighert and Michael Folkes for their
constructive suggestions and comments for this paper.
References
Bandemer H, Gottwald S (1995) Fuzzy Sets, Fuzzy Logic, Fuzzy Methods with
Applications. John Wiley & Sons
Box GEP, Jenkins GM, Reinsel GC (1994) "Time Series Analysis: Forecasting and
Control", 3rd Edition, Holden-Day
Chen DG, Ware DW (1999) A neural network model for forecasting fish stock
recruitment. Can. J. Fish. Aquat. Sei. 56:2385-2396
Chen DG, Hargreaves B, Ware DM, Liu Y (2000) A fuzzy logic model with genetic
algorithrns for analyzing fish stock-recruitment relationships. Can. J. Fish. Aquat. Sei.
57:1878-1887
Chen DG, Irvine JR (2001) A new semiparametric model to exarnine stock-recruitment
relationships incorporating environmental data. Can. J. Fish. Aquat. Sei. 58: 11781186
Davison AC, Hinkley DV (1997) Bootstrap Methods and Their Application. Cambridge
University Press
Efron B, Tibshirani RJ (1993) An Introduetion to the Bootstrap. San Francisco: Chapman &
Hall
Goodwin GC, Sin KS (1984) Adaptive Filtering Prediction and Control. Prentice-Hall,
Englewood Cliffs, NJ
Hilbom R (1985) Simplified caleulation of optimum spawning stock size from Ricker's
stock recruitment curve. Can. J. Fish. Aquat. Sei. 42: 1833-1834
Hilbom R, Walters CJ (1992) Quantitative Fisheries Stock Assessment: Choice, Dynarnics
and Uneertainty. Chapmam & Hall
Hyatt KD, Luedke W, Rankin DP, Gordon L (1994) Review of 1988-1994 foreeast
performance, stock status, and 1995 forecasts of Barkley Sound soekeye. Pacific Stock
Assessment Review Committee. Working paper S94-21, p43
fuzzy sets with an associated degree of membership function based on Takagi and
Sugeno (1983). The inherent uncertainties in the environmental data were taken
into account by the fuzzification process. The Fuzzy-SR model is capable of
empirically approximating the underlying SR relationship, and can also provide a
crisp and simple functional relationship among the inputs and output according to
the fuzzy rules (two in this application). An important feature of the Fuzzy-SR
model is that the functional SR relationships described by the fuzzy rules can be
chosen to more realistically describe the biological processes that affect
recruitment.
Accordingly, the Fuzzy-SR model with the bootstrap resampling algorithm can
be a useful tool for stock recruitment analysis to fish population.
Acknowledgements
I sincerely thank Jim Irvine, Jake Schweighert and Michael Folkes for their
constructive suggestions and comments for this paper.
References
Bandemer H, Gottwald S (1995) Fuzzy Sets, Fuzzy Logic, Fuzzy Methods with
Applications. John Wiley & Sons
Box GEP, Jenkins GM, Reinsel GC (1994) "Time Series Analysis: Forecasting and
Control", 3rd Edition, Holden-Day
Chen DG, Ware DW (1999) A neural network model for forecasting fish stock
recruitment. Can. J. Fish. Aquat. Sei. 56:2385-2396
Chen DG, Hargreaves B, Ware DM, Liu Y (2000) A fuzzy logic model with genetic
algorithrns for analyzing fish stock-recruitment relationships. Can. J. Fish. Aquat. Sei.
57:1878-1887
Chen DG, Irvine JR (2001) A new semiparametric model to exarnine stock-recruitment
relationships incorporating environmental data. Can. J. Fish. Aquat. Sei. 58: 11781186
Davison AC, Hinkley DV (1997) Bootstrap Methods and Their Application. Cambridge
University Press
Efron B, Tibshirani RJ (1993) An Introduetion to the Bootstrap. San Francisco: Chapman &
Hall
Goodwin GC, Sin KS (1984) Adaptive Filtering Prediction and Control. Prentice-Hall,
Englewood Cliffs, NJ
Hilbom R (1985) Simplified caleulation of optimum spawning stock size from Ricker's
stock recruitment curve. Can. J. Fish. Aquat. Sei. 42: 1833-1834
Hilbom R, Walters CJ (1992) Quantitative Fisheries Stock Assessment: Choice, Dynarnics
and Uneertainty. Chapmam & Hall
Hyatt KD, Luedke W, Rankin DP, Gordon L (1994) Review of 1988-1994 foreeast
performance, stock status, and 1995 forecasts of Barkley Sound soekeye. Pacific Stock
Assessment Review Committee. Working paper S94-21, p43