7, No 1īonfig KW, Guirimov BG “Neuro-Fuzzy based generation of fuzzy knowledge and the flexible procedure of logical inference for the graphic pattern recognition, in proc. 1992īerenguer M and Davies P “Credit Risk: Modeling To Manage”. (1992) Applied Statistics for Business and Economics. Theory and practice, Tabriz University, Tabriz, Iran, 1993Īliev, R, Fazlollahi, B, Vahidov, R „Soft Computing Based Multi-Agent Marketing Decision Support System“, The Journal of Intelligent and Fuzzy Systems. 244Īliev RA, Vaguidov RM, Neural networks. Tabriz: Tabriz University Press, 1993, p. World Scientific, New Jersey, London, Singapore, Hong Kong, 2001, 444 pĪliev RA, Mamedova GA, Aliev RR Fuzzy Sets Theory and Its Application. This process is experimental and the keywords may be updated as the learning algorithm improves.Īliev RA, Aliev RR. These keywords were added by machine and not by the authors. An effective stock trading system must use both qualitative and quantitative factors. Also we cannot ignore those factors at all, because technical indexes only are not capable of proper description of a complicated real-world environment. These methods do not give expected results in situations when the data are influenced by subjective factors such as psychological, macro-economical, or political issues. Conventional approaches address Regression and Time Series Analysis methods for stock market prediction. This creates a need for intelligent stock-trading systems that are intended to help the investors make realistic prediction for taking optimal decisions. The stock market is very attractive due to high expected profit.
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