33,914 research outputs found
Fuzzy simulation of forest road surface parameters
The problem of construction of forest roads with the use of local low-strength substandard materials and industrial waste is considered. To solve the problem, the primary task is to develop a method for estimating the parameters of road surfaces taking into account the conditions of uncertainties in the data. This technique allows us to reasonably clarify some of the regulatory parameters and improve the technology of construction of forest roads, which was the goal of the work. To formalize the task, experimental studies were performed and on the basis of these results, the statement of the task of fuzzy derivation of the function for estimating the bearing capacity of the coating was performed. The synthesis of the output function is performed by means of Matlab. © 2019 IOP Publishing Ltd. All rights reserved
A decomposition theorem for maxitive measures
A maxitive measure is the analogue of a finitely additive measure or charge,
in which the usual addition is replaced by the supremum operation. Contrarily
to charges, maxitive measures often have a density. We show that maxitive
measures can be decomposed as the supremum of a maxitive measure with density,
and a residual maxitive measure that is null on compact sets under specific
conditions.Comment: 11 page
Fuzzy games with a countable space of actions and applications to systems of generalized quasi-variational inequalities
In this paper, we introduce an abstract fuzzy economy (generalized fuzzy
game) model with a countable space of actions and we study the existence of the
fuzzy equilibrium. As applications, two types of results are obtained. The
first ones concern the existence of the solutions for systems of generalized
quasi-variational inequalities with random fuzzy mappings which we define. The
last ones are new random fixed point theorems for correspondences with values
in complete countable metric spaces.Comment: 18 page
A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems
This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding
Mode Controller for the control of dynamic uncertain systems. The proposed
controller combines the advantages of Second order Sliding Mode Control, Fuzzy
Logic Control and Adaptive Control. The reaching conditions, stability and
robustness of the system with the proposed controller are guaranteed. In
addition, the proposed controller is well suited for simple design and
implementation. The effectiveness of the proposed controller over the first
order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based
simulations performed on a DC-DC Buck converter. Based on this comparison, the
proposed controller is shown to obtain the desired transient response without
causing chattering and error under steady-state conditions. The proposed
controller is able to give robust performance in terms of rejection to input
voltage variations and load variations.Comment: 14 page
Data granulation by the principles of uncertainty
Researches in granular modeling produced a variety of mathematical models,
such as intervals, (higher-order) fuzzy sets, rough sets, and shadowed sets,
which are all suitable to characterize the so-called information granules.
Modeling of the input data uncertainty is recognized as a crucial aspect in
information granulation. Moreover, the uncertainty is a well-studied concept in
many mathematical settings, such as those of probability theory, fuzzy set
theory, and possibility theory. This fact suggests that an appropriate
quantification of the uncertainty expressed by the information granule model
could be used to define an invariant property, to be exploited in practical
situations of information granulation. In this perspective, a procedure of
information granulation is effective if the uncertainty conveyed by the
synthesized information granule is in a monotonically increasing relation with
the uncertainty of the input data. In this paper, we present a data granulation
framework that elaborates over the principles of uncertainty introduced by
Klir. Being the uncertainty a mesoscopic descriptor of systems and data, it is
possible to apply such principles regardless of the input data type and the
specific mathematical setting adopted for the information granules. The
proposed framework is conceived (i) to offer a guideline for the synthesis of
information granules and (ii) to build a groundwork to compare and
quantitatively judge over different data granulation procedures. To provide a
suitable case study, we introduce a new data granulation technique based on the
minimum sum of distances, which is designed to generate type-2 fuzzy sets. We
analyze the procedure by performing different experiments on two distinct data
types: feature vectors and labeled graphs. Results show that the uncertainty of
the input data is suitably conveyed by the generated type-2 fuzzy set models.Comment: 16 pages, 9 figures, 52 reference
- …