In this paper we proposed a fuzzy neural network model which can
einbody a fuzzy Takagi-Sugeno model and carry out fuzzy inference and support structure of fuzzy rules. The algorithm of model properties improvement consists of several new procedures námely input space partition, fuzzy terms number and rule number extending, low-effective fuzzy terms and rules extraction and consequent structure Identification. A fuzzy neural network is constructed based on fuzzy model. By learning of the neural network we can tuně of embedded initial fuzzy model. To show the applicability of new method and to niake a possibility to reál systerns rnodelling, we designed the fuzzy-neural network prograrnrne tool FUZNET. Next, we perforrned numerical experiment to do fuzzy rnodelling for an artifical tirne series and reál non-linear complex systém.
We tried to determine as accurately as possible the geocentric coordinates of the Hvar Doppler station in the coordinate system of broadcast and in one of precise ephemerides, on the basis of Doppler observations from the project IDOC-82 and of broadcast ephemerides alone. With this aim in view it was necessary in the first place to calculate for the project IDOC-82 two new variations of multipoint solutions by means of broadcast ephemerides (MPBE), taking into account 11 stations, i.e. 10 suitable stations. Coordinates X, Y, Z for the Hvar station contained in this way were thereupon converted from BE-system by means of
three-dimensional Helmert transformation and by using available identical stations from previous projects EDOC-2, ERIDOC and ALGEDOP-82 for all of which multipoint solutions with precise ephemerides (MPPE) are dsposable.
The goal of this paper is to present and defend an inferentialist account of the meaning of fictional names on the basis of Sellars-Brandom’s inferentialist semantics and a Brandomian anaphoric theory of reference. On this inferentialist account, the meaning of a fictional name is constituted by the relevant language norms which provide the correctness conditions for its use. In addition, the Brandomian anaphoric theory of reference allows us to understand reference in terms of anaphoric word-word relations, rather than substantial word-world relations. In this paper I argue that this inferentialist account has many important merits over its rival theories. One important merit is that it explains why we can use fictional names to make true statements, even if they lack bearers. As a consequence, this theory allows us to use fictional names without committing ourselves to an implausible ontology of fictional entities. Another important merit is that it provides a uniform semantic account of fictional names across different types of statements in which fictional names are involved.
An influence of changing the humic acids content on soil water repellency and saturated hydraulic conductivity was studied on soil samples of Mollic Gleysol from Cilizska Radvan in the Danubian Lowland. Water repellency was measured with the water drop penetration time (WDPT) test on original soil samples and on soil samples with increased humic acids content. Saturated hydraulic conductivity coefficient was measured on the above-mentioned samples with falling head permeameter. From the results of measuring it follows that an increasing of humic acids content in soil resulted in an decreasing of the coefficient of saturated hydraulic conductivity of the soil under study. Original soil was non-water reppelent soil. Already a small increasing of humic acids content in soil (in 17.9 % at original amount) caused that the soil became slightly or strongly water repellent in the average of soil moisture 15-30 %. At soil moisture less than 15 % time of penetration decreased probably as a result of shrinking and cracking of the soil. Water repellency of soil samples from horizon 0 - 5 cm was usually higher than water repellency of soil samples from horizon 5 - 10 cm both in case of humic acids extracted from peat and in case of humic acids extracted from the same soil from Cilizska Radvan. and Na pôdnej vzorke čiernice glejovej (ČA G) (MKSP, 2000) z lokality Čiližská Radvaň v Podunajskej nížině bol skúmaný pomocou testu času vsaku kvapky vody (WDPT test) vplyv zmeny obsahu humínových kyselin na vodoodpudivosť a nasýtenú hydraulickú vodivosť pôdy. Vodoodpudivosť bola meraná na pôvodných pôdnych vzorkách a vzorkách zo zvýšeným obsahom humínových kyselín. Koeficient nasýtenej hydraulickej vodivosti bol meraný na týchto pôdnych vzorkách metódou premenlivého hydraulického sklonu. Z nameraných výsledkov vyplýva, že nárast obsahu humínových kyselín v pôde mal za následok pokles koeficientu nasýtenej hydraulickej vodivosti študovanej pôdy. Už malé zvýšenie obsahu humínových kyselín v pôde (o 17,9 % pôvodnej hodnoty ) spôsobilo, že pôda sa stala slabo až silne vodoodpudivou vo vlhkostnom rozsahu 15-30 %. Pri pôdnej vlhkosti nižšej ako 15 % sa čas vsakovania zmenšil pravdepodobne v dôsledku zmršťovania pôdy a vzniku puklín. Vodoodpudivosť pôdnych vzoriek z horizontu 0 - 5 cm vo väčšine prípadov bola vyššia než vodoodpudivosť pôdnych vzoriek z horizontu 5 - 10 cm aj v prípade pridania humínových kyselín, extrahovaných z rašeliny, aj v prípade pridania humínových kyselín, extrahovaných z tej istej pôdy.
The main aim of this paper is to present a new possibility for detection and recognition of different categories of electric and conventional (equipped with combustion engine) vehicles. These possibilities are provided by use of thermal and visual video cameras and two methods of machine learning. The used methods are Haar cascade classifier and convolutional neural network (CNN). The thermal images, obtained through an infrared thermography camera, were used for the training database. The thermal cameras can complement or substitute visible spectrum of video cameras and other conventional sensors and provide detailed recognition and classification data needed for vehicle type recognition. The first listed method was used as an object detector and serves for the localization of the vehicle on the road without any further classification. The second method was trained for vehicle recognition on the thermal image database and classifies a localized object according to one of the defined categories. The results confirmed that it is possible to use infrared thermography for vehicle drive categorization according to the thermal features of vehicle exteriors together with methods of machine learning for vehicle type recognition.
This paper considers the data-based identification of industrial robots using an instrumental variable method that uses off-line estimation of the joint velocities and acceleration signals based only on the measurement of the joint positions. The usual approach to this problem relies on a `tailor-made' prefiltering procedure for estimating the derivatives that depends on good prior knowledge of the system's bandwidth. The paper describes an alternative Integrated Random Walk SMoothing (IRWSM) method that is more robust to deficiencies in such a priori knowledge and exploits an optimal recursive algorithm based on a simple integrated random walk model and a Kalman filter with associated fixed interval smoothing. The resultant IDIM-IV instrumental variable method, using this approach to signal generation, is evaluated by its application to an industrial robot arm and comparison with previously proposed methods.
Profitability of Turkish banking sector gained importance after national and international financial crisis happened in the last decade, which revealed the need to make a research on profitability and the factors determining profitability. In recent years, new techniques of soft computing (SC) like genetic algorithms (GAs), fuzzy logic (FL) and especially artificial neural networks (ANNs) have been applied into the financial domain to solve the domain issues because of their successful applications in nonlinear multivariate situations. An adaptive system was needed due to the fact that insufficient use of application software programs for SC and the fact that single software is only applicable for specific model. Furthermore, even though ANNs have been applied to many areas; little attention has been paid to estimation of bank profitability with ANNs. This article is intended to analyze and estimate the profitability of deposit banks in Turkey with an adaptive software model of ANNs which have not been previously applied for this context, comprehensively. The results from the software model, which processes the factors affecting profitability, indicate that all of the variables used have significant impacts in varying proportions on profitability and that obtained estimations achieved the targeted and acceptable performance of success. This software model is expected to provide easiness on estimating bank profitability, since giving successful estimations and not being affected by user differences. Additionally, it is aimed to construct a software model for being used in different fields of study and financial domain.
We give a short introduction to a method for the data-sparse approximation of matrices resulting from the discretisation of non-local operators occurring in boundary integral methods or as the inverses of partial differential operators. The result of the approximation will be the so-called hierarchical matrices (or short H-matrices). These matrices form a subset of the set of all matrices and have a data-sparse representation. The essential operations for these matrices (matrix-vector and matrixmatrix multiplication, addition and inversion) can be performed in, up to logarithmic factors, optimal complexity.
We obtain necessary conditions for convergence of the Cauchy Picard sequence of iterations for Tricomi mappings defined on a uniformly convex linear complete metric space.