Abstract
Accurate prediction of aquatic product prices can improve the quality of business strategy of aquatic product market. Case-based reasoning (CBR) systems have long been intensively used in several areas of artificial intelligence. But it is difficult to cluster similar cases from case bases as there are uncertainties in knowledge representation, attribute description and similarity measures in CBR. To increase the efficiency and reliability of CBR, fuzzy theories have been combined with CBR. In this paper, fuzzy case-based reasoning (FCBR) has been developed to forecast the price of aquatic products.
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