Traditional air quality assessment is realized using air quality indices which are determined as mean values of selected air pollutants. Thus, air quality assessment depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated, e.g. using systems bas...

Neural Networks (NN), Type-1 Fuzzy Logic Systems (T1FLS) and Interval Type-2 Fuzzy Logic Systems (IT2FLS) are universal approximators, they can approximate any non-linear function. Recent research shows that embedding T1FLS on an NN or embedding IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect information. In this paper we...

In this paper, we propose a novel fuzzy-based framework for routing and wavelength assignment in alloptical DWDM backbone networks with sparse wavelength conversion capabilities. While most of the previous work in this field focuses on optical networks without wavelength conversion with full wavelength conversion capabilities, in this work we study networks with sparse wavelength conversion res...

Vehicular ad hoc networks have been attracting the interest of both academic and industrial communities on account of their potential role in Intelligent Transportation Systems (ITS). However, due to vehicle movement and fading in wireless communications, providing a reliable and efficient multi-hop broadcast service in vehicular ad hoc networks is still an open research topic. In this paper, w...

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.05.040 ⇑ Corresponding author. Tel.: +90 312 2977900. E-mail address: [email protected] (C.H. Alad In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural net...

Submitted: Jun 23, 2013; Accepted: Jul 20, 2013; Published: Jul 25, 2013 Abstract: Peculiar features of development of hybrid adaptive systems using neuro-fuzzy network structures are discussed. Quality and amount of information about an object is insufficient. Classical, adaptive, robust, fuzzy, neural methods of regulator designing have been compared. Problem of parameter adjustment of neuro-...

Recently, artificial neural networks (ANNs) have been extensively studied and used in different areas such as pattern recognition, associative memory, combinatorial optimization, etc. In this paper, we investigate the ability of fuzzy neural networks to approximate solution of a dual fuzzy polynomial of the form a1x+ ...+anx n = b1x+ ...+ bnx n+d, where aj , bj , d ε E 1 (for j = 1, ..., n). Si...

C networks, fuzzy networks and usefulness and applications. V technological re capable of rep1 experience. The concept underlining thei and comparisoi learning algorit architectures a r illustrated with identification, sc diagnosis tool, compression usii prediction, etc. In the later systems, includi Takagi-Sugano building blocks of fuzzy and ne] several applica concluded with chip. Fascination...

Neurofuzzy systems—the combination of artificial neural networks with fuzzy logic—have become useful in many application domains. However, conventional neurofuzzy models usually need enhanced representational power for applications that require context and state (e.g., speech, time series prediction, control). Some of these applications can be readily modeled as finite state automata. Previousl...

In this paper, an adaptive neuro-fuzzy system, called HyFIS, is proposed to build and optimise fuzzy models. The proposed model introduces the learning power of neural networks into the fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training eramples by a ...