One of the important challenges in Graphical models is the problem of dealing with the uncertainties in the problem. Among graphical networks, fuzzy cognitive map is only capable of modeling fuzzy uncertainty and the Bayesian network is only capable of modeling probabilistic uncertainty. In many real issues, we are faced with both fuzzy and probabilistic uncertainties. In these cases, the propo...

Dealing with classification problems in practice often has to cope with uncertain information, either in the training or in the operation phase or both. Modeling these uncertainties allows to enhance the robustness or performance of the classifier. In this paper we focus on the operation phase and present a general, but simple extension to rule based fuzzy classifier to do so. Therefor uncertai...

Fuzzy rule-based systems are universal approximators of non-linear functions [1] as multilayer feedforward neural networks [2]. That is, they have a high approximation ability of non-linear functions. A large number of neural and genetic learning methods have been proposed since the early 1990s [3, 4] in order to fully utilize their approximation ability. Traditionally, fuzzy rule-based systems...

Nodes in sensor networks can be easily compromised by an adversary due to hostile environments in which compromised nodes could be used to inject false reports into the network. The dynamic en-route scheme can detect and drop such false reports during the forwarding phase. In this scheme, choosing a threshold value is important as this gives a trade off between security of power and energy cons...

The growth of cities, climate change, biomes degradation, disease and social issues influence the views of life quality and health of populations. The characteristics of health and environment are usually measured with different and disconnected indicators. However, the importance of working with those two aspects in a joined way is every time larger. In this paper it is presented a fuzzy rule-...

On this paper, an application of a high performance fuzzy logic hardware controller is explained, a model car is given autonomous character. Development of a fuzzy rule base, aided by a simulator program, is followed by trials made to come to conclusions regarding ideal and real functioning of the hardware developed.

A fuzzy logic based classification engine has been developed for classifying mass spectra obtained with an imaging internal source Fourier transform mass spectrometer (ILD-FTMS). Traditionally, an operator uses the relative abundance of ions with specific mass-to-charge (m/z) ratios to categorize spectra. An operator does this by comparing the spectrum of m/z versus abundance of an unknown samp...

Some difficulties emerging during the construction of fuzzy behaviour-based control structures are inherited from the type of the applied fuzzy reasoning. The fuzzy rule base requested for many classical reasoning methods needed to be complete. In case of fetching fuzzy rules directly from expert knowledge e.g. for the behaviour coordination module, the way of building a complete rule base is n...

The aim of this contribution is to point out the importance of interpretability of fuzzy rule-based systems. We try to approach this key property axiomatically, as a starting point, restricting to interpretability of linguistic variables (in Zadeh’s sense). The proposed approach is underlined with an intuitive example. Finally, the benefits of considering interpretability in design and tuning o...