期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：9
出版社：S.S. Mishra
摘要：Online Commodity Trading is a chaotic system for forecasting, since it has no axiom to predict the future price of commodity. The price volatility in the trading is inconsistent which makes the predicting methods such as technical analysis, fundamental analysis, time series analysis and statistical analysis etc. to be uncertain for predicting the price. ANFIS (Adaptive Neuro- Fuzzy Inference System), a universal estimator, combines both neural networks and fuzzy logic principles to invent an inference system that have the capability of learning to approximate the nonlinear functions. The resultant fuzzy inference system gets adapted to the learnt environment and it reflects according to the situations in the future testing. This is a best method to establish the unknown and hidden pattern in the data and hence it effectively interprets the price hike in OCT(online commodity trading). ANFIS PRICE INTERPRETOR SYSTEM incorporate these features for predicting the commodity price pattern using ANFIS, there are two modules, one is training session done by backpropogation algorithm and other is predicting price pattern based on the inference rules(if-then rules) formed by the previously trained data. In this paper, APIS system predicts the price of a commodity-CHANNA, which plays a very important role in human diet in India. It is widely appreciated as health food that offers the most practical means of eradicating protein malnutrition a mong vegetarian children and nursing mothers. By predicting the price of Channa in OCT and by further prediction of future prices after it has been banned in Online commodity trading, it proves that there is no major impact of price hike and the price inflation. This system also suggests not to ban the product; instead some regulatory measures to be taken
