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Über dieses Buch

This carefully edited book contains contributions of prominent and active researchers and scholars in the broadly perceived area of intelligent systems. The book is unique both with respect to the width of coverage of tools and techniques, and to the variety of problems that could be solved by the tools and techniques presented. The editors have been able to gather a very good collection of relevant and original papers by prominent representatives of many areas, relevant both to the theory and practice of intelligent systems, artificial intelligence, computational intelligence, soft computing, and the like. The contributions have been divided into 7 parts presenting first more fundamental and theoretical contributions, and then applications in relevant areas.

Inhaltsverzeichnis

Frontmatter

Fuzzy Systems: Theory

Frontmatter

Prospects for Truth Valuation in Fuzzy Extended Logic

Lotfi Zadeh’s fuzzy extended logic is applied to approximate linguistic reasoning. The prevailing fuzzy reasoning methods still seem to have some bivalent commitments in truth valuation and thus an alternative many-valued resolution is presented at meta-level.
Vesa A. Niskanen

Coherence and Convexity of Euclidean Radial Implicative Fuzzy Systems

The chapter discuss a necessary condition for coherence of radial implicative fuzzy systems. We present the general condition in an implicit form. The condition is based on the value of the minima of a certain function. We show that this function is convex. Further an explicit solution for Euclidean systems is provided.
David Coufal

Fuzzy Systems: Application

Frontmatter

Catastrophe Bond Pricing with Fuzzy Volatility Parameters

The number of natural catastrophes and losses caused by them increase in time. The damages caused by natural disasters are difficult to handle for insurers. Therefore catastrophe bonds were introduced to transfer the catastrophic risk to financial markets. In this paper we continue our research concerning catastrophe bond pricing. In our approach we use stochastic analysis and fuzzy sets theory in order to obtain catastrophe bond pricing formulas. To model the short interest rate we use the one- and two-factor Vasicek model. We take into account different sources of uncertainty, not only the stochastic one. In particular, we treat the volatility of the interest rate and market price of risk as fuzzy numbers. We use Monte Carlo simulations for data describing natural catastrophic events in the United States to illustrate the obtained results.
Piotr Nowak, Maciej Romaniuk

Evaluating Condition of Buildings by Applying Fuzzy Signatures and R-Fuzzy Operations

It is an significant task to qualify and rank residential buildings based on various priority aspects and to make optimum allocation of the material resources available for the renewal of the buildings. To this end a model based on fuzzy logic was prepared. To construct and to test this model many detailed technical-static expert reports were available all related to a stock of residential buildings in Budapest. Based on this report a database was created. With the help of this database a model was prepared, calculating a so-called status characteristic value between 0 and 1 on the basis of the structures and status of the buildings. For this calculation a fuzzy singleton signature model was prepared. Based on this a hierarchy can be set up related to the stock of buildings, which is suitable for supporting the decision-making on intervention. The model was examined by using the created database. Membership values characterising the status of the load-bearing structures—were defined on the basis of the deterioration of the structures. In this chapter a new method for the determination of the membership values is described, which in addition to the deteriorations of the structures takes into account other parameters of the structures, and the impact exerted on the quality of the structure, too. The method is suitable for the determination of the membership values of all primary and secondary structures. As an example the membership values of the foundation structures of the buildings in the database were defined and the results were analysed. The method was elaborated by the use of “real fuzzy values” (R-fuzzy sets), an extension of the concept of classic fuzzy sets, the former being suitable for simultaneously taking into account various aspects.
Ádám Bukovics, László T. Kóczy

The Determination of the Bitrate on Twisted Pairs by Mamdani Inference Method

There are several methods for predicting the available maximal data transfer rate on dedicated telecommunication connections. This chapter presents some generally used techniques for prediction and some results of a Mamdani-type fuzzy reasoning system that is used in a telecommunication research aimed to create new predicting methods. At the end of the article the results of various methods are compared. All presented techniques are used for evaluation of the twisted-pair based local loops of the telecommunication access networks.
Ferenc Lilik, László T. Kóczy

Construction Site Layout and Building Material Distribution Planning Using Hybrid Algorithms

Chapters have been written previously about how genetic algorithms and other evolution-based algorithms could aid construction site layout planning. These articles presented approaches that solved of the layout problem by applying costs on the moving of construction materials across the site. Our goal was to build an algorithm which is specialized in solving problems of distributing building materials—brick for example—on a site by placing their pallets at the optimal spots, for every unit built from a given material to be within optimal reach. This article describes a solution of this problem for the engineering practice and interprets the slow but accurate method of the Hungarian Algorithm, further it proposes a Memetic Algorithm as a faster but almost as accurate solution. Conclusions are drawn about the usability of this method.
Bence Kalmár, András Kalmár, Krisztián Balázs, László T. Kóczy

Neural Network

Frontmatter

Accuracy of Surrogate Solutions of Integral Equations by Feedforward Networks

Surrogate solutions of Fredholm integral equations by feedforward neural networks are investigated theoretically. Convergence of surrogate solutions computable by networks with increasing numbers of computational units to theoretically optimal solutions is proven and upper bounds on rates of convergence are derived. The results hold for a variety of computational units, they are illustrated by examples of perceptrons and Gaussian radial units.
Věra Kůrková

Vehicle Classification Using Neural Networks with a Single Magnetic Detector

In this work, principles of operation, advantages and disadvantages are presented for different detector technologies. An idea of a new detection and classification method for a single magnetic sensor based system is also discussed. It is important that the detection algorithm and the neural network classifier needs to be easily implementable in a microcontroller based system.
Peter Šarčević

Clustering and Image Processing

Frontmatter

Exemplary Applications of the Complete Gradient Clustering Algorithm in Bioinformatics, Management and Engineering

This publication deals with the applicational aspects and possibilities of the Complete Gradient Clustering Algorithm—the classic procedure of Fukunaga and Hostetler, prepared to a ready-to-use state, by providing a full set of procedures for defining all functions and the values of parameters. Moreover, it describes how a possible change in those values influences the number of clusters and the proportion between their numbers in dense and sparse areas of data elements. The possible uses of these properties were illustrated in practical tasks from bioinformatics (the categorization of grains for seed production), management (the design of a marketing support strategy for a mobile phone operator) and engineering (the synthesis of a fuzzy controller).
Piotr Kulczycki, Malgorzata Charytanowicz, Piotr A. Kowalski, Szymon Łukasik

A Hierarchical Approach for Handwritten Digit Recognition Using Sparse Autoencoder

Higher level features learning algorithms have been applied on handwritten digit recognition and got more promising results than just using raw intensity values with classification algorithms. However, the approaches of these algorithms still not take the advantage of specific characteristics of data. We propose a new method to learn higher level features from specific characteristics of data using sparse autoencoder. The main key of our appoarch is to divide the handwritten digits into subsets corresponding to specific characteristics. The experimental results show that the proposed method achieves lower error rates and time complexity than the original approach of sparse autoencoder. The results also show that the more correlated characteristics we define, the better higher level features we learn.
An T. Duong, Hai T. Phan, Nam Do-Hoang Le, Son T. Tran

Fuzzy Single-Stroke Character Recognizer with Various Rectangle Fuzzy Grids

In this chapter we introduce the results of a formerly published FUBAR character recognition method with various fuzzy grid parameters. The accuracy and efficiency of the handwritten single-stroke character recognition algorithm with different sized rectangle (N \(\times \) M) fuzzy grids are investigated. The results are compared to other modified FUBAR algorithms and known commercial and academic recognition methods. Possible applications and further extensions are also discussed. This work is the extended and fully detailed version of a previously published abstract.
Alex Tormási, László T. Kóczy

Robotic Systems

Frontmatter

Delay and Stiffness Dependent Polytopic LPV Modelling of Impedance Controlled Robot Interaction

Impedance/admittance control algorithms are considered as key technologies in human–robot interaction and other fields of advanced robotics where complex physical interaction plays role. In this chapter, we utilize a Tensor Product (TP) Model Transformation based method to derive the delay and stiffness dependent polytopic LPV representation of the impedance controlled physical interaction. The applied transformation method is feasible with bounded delay that is the non-linear function of the environmental stiffness. Thus, the ideal transformation space is non-rectangular that makes it improper for the TP model transformation. We propose a dimensionless parametrisation to define a rectangular grid upon witch the transformation is viable. The resulted model form is promptly appropriate for the modern multi-objective LMI based control design techniques.
József Kuti, Péter Galambos, Péter Baranyi

Local Center of Gravity Based Gathering Algorithm for Fat Robots

Swarm intelligence has become an intensively studied research area in the last few years. Gathering of mobile robots is one of its basic topics that aims the assembly of the scattered robots on the smallest possible area. In this chapter, we present a new and effective algorithm for this task supposing an obstacle-free plan, limited visibility, and synchronous, fat (disc-like) robots without global navigation, communication, or memory. The key idea of the algorithm is that in case of each robot after detecting the visible neighbouring robots it sets the target of the next step based on the encountered visible robots’ center of gravity. The new algorithm was successfully tested using computer simulations for several parameter and swarm size values and it achieved similar or better performance as the studied previously published algorithms in all of the cases.
Kálmán Bolla, Zsolt Csaba Johanyák, Tamás Kovács, Gábor Fazekas

Intelligent Robot Cooperation with Fuzzy Communication

Designing the decision-making engine of a robot which works in a collaborative team is a challenging task. This is not only due to the complexity of the environment uncertainty, dynamism and imprecision, but also because of the coordination of the team has to be included in this design. The robots must be aware of other robots’ actions in order to cooperate and to successfully achieve their common goal. In addition, decisions must be made in real-time and using limited computational resources. In this chapter we propose some novel algorithms for action selection in ambiguous tasks where the communication opportunities among the robots are very limited.
Á. Ballagi, L. T. Kóczy, C. Pozna

Indoor Pose Estimation Using 3D Scene Landmarks for Service Robotics

In this paper, a markerless approach for estimating the pose of a robot using only 3D visual information is presented. As opposite to traditional methods, our approach makes use of 3D features solely for determining a relative position between the imaged scene (e.g. landmarks present on site) and the robot. Such a landmark is calculated from stored 3D map of the environment. The recognition of the landmark is performed via a 3D Object Retrieval (3DOR) search engine. The presented pose estimation technique produces a reliable and accurate pose information which can be further used for complex scene understanding and/or navigation. The performance of the proposed approach has been evaluated against a traditional marker-based position estimation library.
Tiberiu T. Cocias, Sorin M. Grigorescu, Florin Moldoveanu

Data Manipulation

Frontmatter

A Novel View of Bipolarity in Linguistic Data Summaries

The problem of data summarization of a set of (numeric) data, notably a (relational) database is dealt with. We are concerned with how to devise a short, (quasi) natural language summary, in the form of a sentence, which would best grasp the very content of the set of data. For instance, for a personnel database with records corresponding to particular employees who are described by attributes like age, sex, salary, etc., such a linguistic summary with respect to age may be “most employees are middle aged”. We use as a point of departure a fuzzy logic based approach to linguistic summarization originated by Yager, and then developed by Kacprzyk and Zadrożny who have also indicated—first—an intrinsic connection between linguistic summarization and fuzzy querying, and—second—a crucial role of protoforms in Zadeh’s sense. The second point of departure is the concept of a bipolar query in the sense that the querying criteria may be mandatory and optional, i.e. those which must be satisfied and those which should be satisfied if possible, as initiated by Zadrożny, and then developed by Zadrożny, Kacprzyk and De Tré. In this paper we present the concept of a bipolar linguistic summary that combines the very concepts of a linguistic summary with that of bipolarity in the above sense, and also an analogous relation between the linguistic summaries and bipolar queries.
Janusz Kacprzyk, Sławomir Zadrożny, Mateusz Dziedzic

On Reduction of Data Series Dimensionality

In this paper we introduce a complex procedure of reducing dimensionality of multidimensional data series. The procedure consists of several steps, and each step gives a new data series representation as well as dimension reduction. The approach is based on the concept of data series aggregated envelopes, and principal components called here ‘essential attributes’ generated by a multilayer neural network. The essential attributes are generated by outputs of hidden layer neurons. Next, all differences of the essential attributes are treated as new attributes. The real values of the new attributes are nominalized in order to obtain a nominal representation of data series. The approach creates a nominal representation of the original data series and considerably reduces their dimension. Practical verification of the proposed approach was verified for classification and clustering of time series problems, the results are set out in different papers of the authors. Here, the short summarization confirms utilities of time series dimension reduction procedure.
Maciej Krawczak, Grażyna Szkatuła

Bayesian Classification of Interval-Type Information

The subject of Bayes classification of imprecise multidimensional information of interval type by means of patterns defined through precise data (i.e. deterministic or sharp) is investigated here. To this aim the statistical kernel estimators methodology was applied, which avoids the pattern shape for the resulting algorithm. In addition, elements of pattern sets which have insignificant or negative influence on correctness of classification are eliminated. The concept for realizing the procedure is based on the sensitivity method, used in the domain of artificial neural networks. As a result of this procedure the number of correct classifications and—above all—calculation speed increased significantly. A further growth in quality of classification was achieved with an algorithm for the correction of classifier parameter values.
Piotr Kulczycki, Piotr A. Kowalski

Reduction of Dimension and Size of Data Set by Parallel Fast Simulated Annealing

A universal method of dimension and sample size reduction, designed for exploratory data analysis procedures, constitutes the subject of this paper. The dimension is reduced by applying linear transformation, with the requirement that it has the least possible influence on the respective locations of sample elements. For this purpose an original version of the heuristic Parallel Fast Simulated Annealing method was used. In addition, those elements which change the location significantly as a result of the transformation, may be eliminated or assigned smaller weights for further analysis. As well as reducing the sample size, this also improves the quality of the applied methodology of knowledge extraction. Experimental research confirmed the usefulness of the procedure worked out in a broad range of problems of exploratory data analysis such as clustering, classification, identification of outliers and others.
Piotr Kulczycki, Szymon Łukasik

Control

Frontmatter

Intelligent Computations in an Agent-Based Prosumer-Type Electric Microgrid Control System

The growing number of small prosumers, progress in construction of the renewable energy sources and opening of the energy markets lead to development of the concept of a microgrid. To increase efficiency of electricity consumption, production, and trading, energy managing systems (EMS) are being developed. In this paper we present a project of a complex EMS that will combine load scheduling, power balancing and smart trading methods to optimize electric energy costs of running a simulated research and education center. We present a concept of a distributed Agent-based Power Balancing System that controls the power flow in a microgrid by decentralized and distributed decision making. The program optimizes the operating (exploitation) cost of the devices in the microgrid by internally balancing, as much as possible, the produced and consumed power and by trading the remaining energy excesses or deficits on the external market. Agents associated with the devices cooperate using communication protocols. Their aim is to use the energy from renewable energy sources whenever it is only available, and at the same time limit use of the energy from the sources that are more expensive and less environment friendly.
Weronika Radziszewska, Zbigniew Nahorski, Mirosław Parol, Piotr Pałka

Test Generation for Short-Circuit Faults in Digital Circuits

In the first part, the paper presents a test calculation principle which serves for producing tests of logic faults in digital circuits. The name of the principle is composite justification. The considered fault model includes stuck-at-0/1 logic faults. Both single and multiple faults are included. In this paper only combinational logic is taken into consideration. The computations are performed at the gate level. The calculation principle is comparatively simple. It is based only on successive line-value justification, and it yields an opportunity to be realized by an efficient computer program. The first part serves for presenting the basic principle which is used in the second part of the paper. The second part deals with another fault class, namely, short-circuit or bridging faults. A short circuit is an erroneous galvanic connection between two circuit lines. Here, a new algorithm is presented for generating tests, where the composite justification is extended to handle this type of faults, as well.
József Sziray
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