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The objective of this paper is to show the strength of a modified version of particle swarm optimization (PSO) in definition of suitable partitions of fuzzy time series forecasting and increasing its accuracy. Although a lot of contributions have been made to increase the quality of forecasts using fuzzy time series , there are only a few papers considering tuning the length of intervals in forecasting...
This paper presents a multi-agent system (MAS) for reduction of the bullwhip effect in fuzzy supply chains. First, it is shown that, even using an optimal ordering policy, without data sharing the bullwhip effect still exists in the supply chain. Then a multi-agent system is proposed to manage the bullwhip effect. The multi-agent system has four different types of agents. The multi-agent system applies...
We present an approach to a more efficient generation of linguistic summaries using the traditional degree of truth and a degree of focus based mechanism for discarding nonpromising summaries. We use the approach to derive linguistic data summaries to subsume the past performance of an investment (mutual) fund, and then present numerical results on the efficiency of the truncation process proposed...
This paper presents an approach for time series forecasting using a new class of fuzzy neural networks called uninetworks. Uninetworks are constructed using a recent generalization of the classic and and or logic neurons. These generalized logic neurons, called unineurons, provide a mechanism to implement general nonlinear processing and introduce important characteristics of biological neurons such...
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