Comparative analysis of Kolmogorov ANN and process characteristic input-output modes

In the past decades, representation models of dynamical processes have been developed via both traditional math-analytical and less traditional computational-intelligence approaches. This challenge to system sciences goes on because essentially involves the mathematical approximation theory. A comparison study based on cybernetic input-output view in the time domain on complex dynamical processes has been carried out. An analytical decomposition representation of complex multi-input-multi-output thermal processes is set relative to the neural-network approximation representations, and shown that theoretical background of both emanates from Kolmogorov's theorem. The findings provided a new insight as well as highlighted the efficiency and robustness of fairly simple industrial digital controls, designed and implemented in the past, inherited from input-output decomposition model approximation employed

Volatility forecasting using deep neural network with time-series feature embedding

Volatility is usually a proxy indicator for market variation or tendency, containing essential information for investors and policymakers. This paper proposes a novel hybrid deep neural network model (HDNN) with temporal embedding for volatility forecasting. The main idea of our HDNN is that it encodes one-dimensional time-series data as two-dimensional GAF images, which enables the follow-up convolution neural network (CNN) to learn volatility- related feature mappings automatically. Specifically, HDNN adopts an elegant end-to-end learning paradigm for volatility forecasting, which consists of feature embedding and regression components. The feature embedding component explores the volatility-related temporal information from GAF images via the elaborate CNN in an underlying temporal embedding space. Then, the regression component takes these embedding vectors as input for volatility forecasting tasks. Finally, we examine the feasibility of HDNN on four synthetic GBM datasets and five realworld Stock Index datasets in terms of five regression metrics. The results demonstrate that HDNN has better performance in most cases than the baseline forecasting models of GARCH, EGACH, SVR, and NN. It confirms that the volatility-related temporal features extracted by HDNN indeed improve the forecasting ability. Furthermore, the Friedman test verifies that HDNN is statistically superior to the compared forecasting models

Output tracking control for class of fuzzy time-delay systems

Dimirovski, Georgi M. (Dogus Author)The output tracking control problem for fuzzy time-delay systems in presence of parameter perturbations has been solved via fuzzy T-S system models and variable-structure control approach. Following the reaching condition, a variable-structure fuzzy control method is proposed accordingly, when the time delay is known and available and when unknown and unavailable. The method guarantees the system operation arrives to the sliding surface in finite time interval and be kept there thereafter while tracking the desired trajectory. The sufficient condition for globally bounded state is derived by using the ISS theory and the LMI method. A simulation example demonstrates the validity and effectiveness of the proposed method.16th Triennial World Congress of International, Federation of Automatic Control, IFAC 200