6,664 research outputs found
New directions in fuzzy automata
AbstractAutomata are the prime example of general computational systems over discrete spaces. The incorporation of fuzzy logic into automata theory resulted in fuzzy auotomata which can handle continuous spaces. Moreover, they are able to model uncertainty which is inherent in many applications. Deterministic Finite-state Automata (DFA) have been the architecture, most used in many applications, but, the increasing interest in using fuzzy logic for many new areas necessitates that the formalism of fuzzy automata be more developed and better established to fulfill implementational requirements in a well-defined manner. This need is due to the fact that despite the long history of fuzzy automata and lots of research being done on that, there are still some issues which have not been well-established and issues which need some kind of revision. In particular, we focus on membership assignment, output mapping, multi-membership resolution, and the concept of acceptance for fuzzy automata. We develop a new general definition for fuzzy automata, and based on that, develop well-defined and application-driven methodologies to establish a better ground for fuzzy automata and pave the way for forthcoming applications
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
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A combined neuro fuzzy-cellular automata based material model for finite element simulation of plane strain compression
This paper presents a modelling strategy that combines Neuro-Fuzzy methods to define the material model with Cellular Automata representations of the microstructure, all embedded within a Finite Element solver that can deal with the large deformations of metal processing technology. We use the acronym nf-CAFE as a label for the method. The need for such an approach arises from the twin demands of computational speed for quick solutions for efficient material characterisation by incorporating metallurgical knowledge for material design models and subsequent process control. In this strategy, the cellular automata hold the microstructural features in terms of sub-grain size and dislocation density which are modelled by a neuro-fuzzy system that predicts the flow stress. The proposed methodology is validated on a two dimensional (2D) plane strain compression finite element simulation with Al-1% Mg alloy. Results from the simulations show the potential of
the model for incorporating the effects of the underlying microstructure on the evolving flow stress fields. In doing this, the paper highlights the importance of understanding the local transition rules that affect the global behaviour during deformation
Retraction and Generalized Extension of Computing with Words
Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a
formal model of computing with values. Motivated by Zadeh's paradigm of
computing with words rather than numbers, Ying proposed a kind of fuzzy
automata, whose input alphabet consists of all fuzzy subsets of a set of
symbols, as a formal model of computing with all words. In this paper, we
introduce a somewhat general formal model of computing with (some special)
words. The new features of the model are that the input alphabet only comprises
some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy
transition function can be specified arbitrarily. By employing the methodology
of fuzzy control, we establish a retraction principle from computing with words
to computing with values for handling crisp inputs and a generalized extension
principle from computing with words to computing with all words for handling
fuzzy inputs. These principles show that computing with values and computing
with all words can be respectively implemented by computing with words. Some
algebraic properties of retractions and generalized extensions are addressed as
well.Comment: 13 double column pages; 3 figures; to be published in the IEEE
Transactions on Fuzzy System
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