Anytime information processing based on fuzzy and neural network models

Abstract

In modern measurement and control systems, the available time and resources are often not only limited, but could change during the operation of the system. In these cases, the so called anytime algorithms could be used advantageously. While diflerent soft computing methods are wide-spreadly used in system modeling, their usability in these cases are limited, because the lack of an universal method for the determination of the needed complexity often results in huge and redundant neural networks1 fuzzy rule-bases. This paper proposes a possible way to carry out anytime information processing in fuzzy systems or neural networks, with the help of the Singular Value Decomposition (SVD)-based complexity reduction algorithm.

Improvement for anytime systems with modular architecture

“…In many cases, this leads to a solution, where a given problem is divided into sub problems. The basics of the procedure have been published in [13] and [14]. In [14] an anytime modular architecture is suggested which is composed of modules realizing the subtasks of a given problem.…”

“…In classical anytime systems ( [13]) a performance profile is assigned to the modules. In the modular architecture proposed in [14], the performance profiles are assigned to the units. The complexity of the anytime system is directly influenced by the number of modules together with the possible connections among them.…”

“…However, using only interruptible algorithms, significantly limits the application area of anytime methods which resulted in the use of contract algorithms [6]. The anytime concept was further generalized to data insufficiency in 1998 [7] and a universal modular frame for contract type anytime systems has been defined in [8]. Today, contract algorithms may have a possibly even more significant role in anytime systems, however interruptible algorithms have remained very popular because of their easy handling and less information need.…”

SVD-based complexity reduction to TS fuzzy models

“…Techniques offering tools to extract the more pertinent elements and to filter out unnecessary or weakly contributing parts of a given rule set are, hence, highly desirable. Among the wide variety of known complexity reduction techniques that may contribute in overcoming the above burden, we focus in this work on the singular value decomposition (SVD) technique because it also offers an excellent extension for "anytime" operations [35]. This paper is hence an attempt to investigate a TS fuzzy observer and model from the complexity and possible complexity reduction point of view.…”

“…The TS fuzzy model-based approaches combined with the SVD technique are excellent tools for "anytime" operations [35]. By using SVD, not only the "sequence" of the rules is defined but also the extent to which they contribute to the mapping.…”