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In this paper we consider a hybrid approach to forecasting time series using neuro-fuzzy prediction models and Fuzzy Cognitive Maps. Main idea of proposed approach is hybridization two different ways for time series forecasting. We can make quantitative and qualitative forecast. In addition, we describes the different approaches to learning and optimization of the network, such as the methods of particle...
This paper presents a comparison between two stochastic, population based and real-valued algorithms. These algorithms are namely Differential Evolution (DE) and Particle Swarm Optimization (PSO). These algorithms are used in the training of feed-forward neural network to be used in the prediction of the daily stock market prices. Stock market prediction is the act of trying to determine the future...
The problem of nonlinear time series prediction using integrated intelligent methods based on support vector machine (SVM) and particle swarm optimization (PSO) is studied. Aiming to the open problems of nonlinear time series prediction such as the best number of historical points and parameters of SVM are hard to be determined, a novel model for time series prediction based on PSO and SVM models...
Short-term electricity demand forecasting for the next hour to several days out is one of the most important tools by which an electric utility plans and dispatches the loading of generating units in order to meet system demand. But there exists chaos in electricity systems to a great extent. Complicated electricity systems are nonlinear systems and the forecasting is very complex in nature and quite...
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