Каскадні нейро-нечіткі мережі в задачах прогнозування на ринках цінних паперів - Zaychenko, Yuriy P.; Hamidov, Galib (The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2017)
A forecasting problem at the stock exchange is considered. For its solution the application of a cascade neo-fuzzy neural network (CNFNN) is suggested. The architecture of the neo-fuzzy neuron and architecture of CNFNN is presented. Training algorithms of CNFNN in packet mode and on-line are described and discussed. The experimental investigations of CNFNN for market index forecasting at the German stock exchange are carried out. During experiments, the number of cascades, inputs, linguistic terms, and the training-to-test ratio of samples were varied. In the experiments, the optimal values of the aforesaid parameters of the training algorithm were found. The comparative experiments estimating forecasting efficiency of the cascade neo-fuzzy neural network and FNN ANFIS were carried out.