Adaptive polynomial neuronetwork predicting model of time series and its training - Мантула, Елена Вадимовна; Машталир, Сергей Владимирович (Eastern-European Journal of Enterprise Technologies, 2014)
The relevance to develop new predicting methods is caused by their vital importance in solving various tasks of industrial, agricultural, financial-economic, medico-biologic and ecological systems. The problem of predicting non-stationary non-linear time series under limited amount of a priori information is considered in the paper. To solve it, the method for synthesizing polynomial neural networks, which is an alternative to multilayer perceptrons and radial-basis neural networks, the use of which has several drawbacks, limiting their use in solving many practical problems is proposed. The advantage of the proposed predicting method over traditional neural networks is the ease of numerical implementation, essential reduction in time to perform the operation, this method allows to handle significantly non-stationary processes, containing both irregular trends, and sudden jumps, and allows to complicate the architecture of neural networks without the need to recalculate already adjusted synaptic weights. Training by epochs, used in training multilayer networks can be used for training such neural network. That is why, since only one hidden layer is studied, the considered neuronetwork model is still configured faster than the standard three-layer perceptron.