Data Mining-Based Identification of Nonlinear Systems

This chapter presents identification methods using associative search of analogs and wavelet analysis. It investigates the properties of data mining-based identification algorithms which allow to predict: (i) the approach of process variables to critical values and (ii) process transition to chaotic dynamics. The methods proposed are based on the modeling of human operator decision-making. The effectiveness of the methods is illustrated with an example of product quality prediction in oil refining. The development of fuzzy analogs of associative identification models is further discussed. Fuzzy approach expands the application area of associative techniques. Finally, state prediction techniques for manufacturing resources are developed on the basis of binary models and a machine learning procedure, which is named associative rules search

Preface to the Special Issue on “Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control”—Special Issue Book

Starting our work on this Special Issue, we assumed that the research results presented here would reflect the solutions to various problems related to production management; however, the set of identified problems showed that their solutions could be useful for a wider range of applications [...

Analysis and Prediction of Electric Power System’s Stability Based on Virtual State Estimators

The stability of bilinear systems is investigated using spectral techniques such as selective modal analysis. Predictive models of bilinear systems based on inductive knowledge extracted by big data mining techniques are applied with associative search of statistical patterns. A method and an algorithm for the elementwise solution of the generalized matrix Lyapunov equation are developed for discrete bilinear systems. The method is based on calculating the sequence of values of a fixed element of the solution matrix, which depends on the product of the eigenvalues of the dynamics matrix of the linear part and the elements of the nonlinearity matrixes. A sufficient condition for the convergence of all sequences is obtained, which is also a BIBO (bounded input bounded output) systems stability condition for the bilinear system

Digital Identification Algorithms for Primary Frequency Control in Unified Power System

The article studies and develops the methods for assessing the degree of participation of power plants in the general primary frequency control in a unified energy system (UES) of Russia based on time series analysis of frequency and power. To identify the processes under study, methods of associative search are proposed. The methods are based on process knowledgebase development, data mining, associative research, and inductive learning. Real-time identification models generated using these algorithms can be used in automatic control and decision support systems. Evaluation of the behavior of individual UES members enables timely prevention of abnormal and emergency situations. Methods for predictive diagnostics of generating equipment in terms of their readiness to participate in the primary frequency control are also proposed. In view of the non-stationarity of the load in electrical networks, the algorithms have been developed using wavelet analysis. Case studies are given showing the operating of the proposed methods

Guest Editorial. Special section: Planning and control of manufacturing and logistic systems

International audienc

Discrete Predictive Models for Stability Analysis of Power Supply Systems

The paper offers an approach to the investigation of the dynamics of nonlinear non-stationary processes with the focus on the risk of dynamic system stability loss. The risk is assessed on the basis of the accumulated knowledge about power supply system operation. New methods for power supply modes analysis are developed and applied as follows: linear discrete point knowledge-based models are developed for nonlinear non-stationary objects; wavelet analysis is used for non-stationary processes; stability loss risks are analyzed through the investigation of spectral decompositions of Gramians of these linear predictive models. Case studies are included

Proceedings of the 7th IFAC Conference on Manufacturing Modelling, Management, and Control

International audienc

Intelligent Identification Algorithms for Frequency/Power Control in Smart Grid

IFAC-PapersOnline.net (ISSN 1474-6670)International audienceThe paper presents intelligent identification methods for Smart Grid based on Wide Area Measuring Systems (WAMS) technology. The methods are based on intelligent process knowledge analysis. The knowledgebase is created and extended in real-time process operation. Intelligent algorithms are offered for predicting power plants dynamics in the tasks of generating facilitys operation optimization. The operators decision-making process is modeled by associative search algorithms

New frontiers in information and production systems modelling and analysis: incentive mechanisms, competence management, knowledge-based production

This book demonstrates how to apply modern approaches to complex system control in practical applications involving knowledge-based systems. The dimensions of knowledge-based systems are extended by incorporating new perspectives from control theory, multimodal systems and simulation methods.  The book is divided into three parts: theory, production system and information system applications. One of its main focuses is on an agent-based approach to complex system analysis. Moreover, specialised forms of knowledge-based systems (like e-learning, social network, and production systems) are introduced with a new formal approach to knowledge system modelling.   The book, which offers a valuable resource for researchers engaged in complex system analysis, is the result of a unique cooperation between scientists from applied computer science (mainly from Poland) and leading system control theory researchers from the Russian Academy of Sciences’ Trapeznikov Institute of Control Sciences