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
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초록
부산공동어시장에서 위판되는 고등어의 일일 위판단가 변동을 인공신경망 기법을 활용하여 예측 실험하였다. 위판량과 위판가격 두가지의 한정된 시계열 입력변수로부터 가상의 입력변수를 생성하여 예측에 필요한 입력변수를 증대시켜 활용함으로써 시계열 자료의 분석 및 예측에 적합한 인공신경망 네트워크의 구축이 가능해졌으며, 일단위의 고등어 위판가격의 예측이 가능함을 알 수 있었다. 본 연구에서는 어획량에 대한 예측에 대하여 언급하지 않았으나, 위판가격 예측의 결과로부터 기후변화와 관련된 물리, 생물학적 해양환경 및 기타 환경 변수의 첨가가 이루어지게 되면 보다 고정도의 어획량 변동 예측이 가능하리라 생각한다. 또한, 어획량의 제한이 설정된 TAC 제도 실시 이후의 시기에 대해서도 양적인 측면과 질적인 측면에 대하여 분석이 필요한 것으로 생각된다.
Abstract
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.
참고문헌 (20)
- Aoki, I., "Neuro-computing for Forecasting the Catch of Young Sa", Bull. Japan. Soc. Fish. Oceanogr., 56(2), pp.113~120 (1992)
- Aoki, I., "Prediction of response of zooplankton biomass to climatic and oceanic change", Ecological Modelling, 120(2), pp.261~270 (1999)
- Asoh, H., "Mathematical Properties of Neural Networks", Jr. Japan. Soc. Artificial Intelligence, 4(2), pp.128~133 (1989)
- Charef, A., "Classification of fish schools based on evaluation of acoustic descriptor characteristics", Fish. Sci, 76(1), pp.1~11 (2010)
- Czerwinski, I.A., "Short-term forecasting of halibut CPUE: Linear and non-linear univariate approaches", Fish. Res, 86(2), pp.120~128 (2007)
- Esmaeili, A., "Prediction of shrimp growth using an artificial neural network and regression models", Aquacult Int, 19(4), pp.705~713 (2010)
- Fantin-Cruz, I., "Zooplankton Density Prediction in a Flood Lake (Pantanal -Brazil) Using Artificial Neural Networks", Internat. Rev. Hydrobiol, 95(4), pp.330~342 (2010)
- Hauser-Davis, R. A., "Case study: Comparing the use of nonlinear discriminating analysis and Artificial Neural Networks in the classification of three fish species: acaras (Geophagus brasiliensis), tilapias (Tilapia rendalli) and mullets (Mugil liza)", Ecological Inforamtics, 5(6), pp.474~478 (2010)
- Hunabashi M., "Introduction for neuro-computing", pp.152~ (1992), Ohmsha
- Hwang, K., "An approach to neurocomputing for the forecast of the catches of multiple species in the set net of Seishyo region, western Sagami Bay", Nippon Suisan Gakkaishi, 63(2), pp.549~556 (1997)
- Hwang, K., "Forecasting for the catch of jack mackerel inthe Komekami set net by a neural network", Bull.Japan. Soc. Fish. Oceanogr, 60(2), pp.136~142 (1996)
- Jang J. -S. R., "Neurofuzzy and soft computing: a cmoputational approach to learning and machine intelligence", Prentice-Hall, pp.614~ (1997)
- Kim D. S., "Theory and application of neural network", pp.387~ (1993), Hightech-info
- Lin C.T, "Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems", pp.797~ (1995), Prentice-Hall
- Matsuba H., "Information processing by neural system", Shokoutou (1993)
- Robotham, H., "Acoustic identification of small pelagic fish species in Chile using support vector machines and neural networks", Fish. Res, 102(1), pp.115~122 (2010)
- Simpson, P. K., "Artificial Neural Systems", pp.209~ (1990), Pergamon Press
- Smith, M., "Neural Networks for Statistical Modeling" (1996), Internationa Thompson computer press
- Yáñez E., "Anchovy (Engraulis ringens) and sardine (Sardinops sagax) abundance forecast off northern Chile: A multivariate ecosystemic neural network approach", Progress in Oceanography, 87(1), pp.242~250 (2010)
- Yoo S., "Forecasting of hairtail(Trichiurus lepturus) landings in Korean waters bytimes series analysis", Bull. Kor. Fish. Soc., 26(4), pp.363~368 (1993)