Guest Editorial Evolving Fuzzy Systems - Preface to the Special Section

I T IS a well-recognized fact that the theory of fuzzy sets and systems, for the last four decades after the seminal paper by Professor Zadeh [1], has demonstrated its remarkable ability to go beyond conventional information representation. It resulted in a wide range of new formulations of practical problems, such as fuzzy control, fuzzy clustering and classification , fuzzy modeling, and fuzzy optimization [2]. Historically, the design of the fuzzy systems has been initially assumed to be centered on expert knowledge [3]. During the 1990s, a new trend emerged [4], [5] that offered techniques to make use of the experimental data. This data-centered approach can be used to enhance and validate the existing expert knowledge or can also be used to substitute its lack (as is the case with autonomous systems, for example). Neurofuzzy and hybrid learning systems were introduced, where fuzzy representation was integrated into a neural learning architecture to bring linguistic meaning of the learned information [5]. An important research challenge today is to develop such techniques and methodologies that allow highly adaptive systems to be self-developed from streaming data (collected in real-time from sensory inputs, the Internet, the environment, or the system monitoring itself). Conventional adaptive systems known from control theory are usually applicable to a very narrow range of conditions (usually linear systems, specific Gaus-sian assumptions for noise and signals, etc.). Moreover, they address the problems of adjustment of the system parameters and coefficients only and normally consider the system structure as a given prefixed entity. Fuzzy (and neurofuzzy) systems offer an enormous area for exploitation with the concept of fuzzily interlinked multimodel substructures that can be simple (e.g., linear) and thus, tractable. The newly emerging concept of dynamically evolving structures, which was, to some extent, already applied to neural networks [6], brought a powerful new concept of evolving fuzzy systems (EFSs). EFSs combine: 1) the interpolation abilities of the fuzzy systems; 2) their flexibility; 3) the linguistic interpretability of the fuzzy systems; and 4) the adaptive feature of the online learning techniques. This new topic was introduced during the last decade [7], [8], [10] and quickly numerous applications to problems of mod-eling, control, prediction, classification, and data processing in a dynamically changing and evolving environment were also developed, including some successful industrial applications [9]–[17]. One can formulate EFS as a synergy between fuzzy (or neuro-fuzzy) systems as expandable (evolvable) structures for information representation and …