인공신경망을 활용한 고등어의 위판가격 변동 예측 -어획량 제한이 없었던 TAC제도 시행 이전의 경우-

Forecasting common mackerel auction price by artificial neural network in Busan Cooperative Fish Market before introducing TAC system in Korea

  • 황강석 (국립수산과학원 자원관리과) ;
  • 최정화 (국립수산과학원 자원관리과) ;
  • 오택윤 (국립수산과학원 자원관리과)
  • 투고 : 2011.09.14
  • 심사 : 2011.12.17
  • 발행 : 2012.02.28


Using artificial neural network (ANN) technique, auction prices for common mackerel were forecasted with the daily total sale and auction price data at the Busan Cooperative Fish Market before introducing Total Allowable Catch (TAC) system, when catch data had no limit in Korea. Virtual input data produced from actual data were used to improve the accuracy of prediction and the suitable neural network was induced for the prediction. We tested 35 networks to be retained 10, and found good performance network with regression ratio of 0.904 and determination coefficient of 0.695. There were significant variations between training and verification errors in this network. Ideally, it should require more training cases to avoid over-learning, which leads to improve performance and makes the results more reliable. And the precision of prediction was improved when environmental factors including physical and biological variables were added. This network for prediction of price and catch was considered to be applicable for other fishes.


연구 과제 주관 기관 : 국립수산과학원


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