Intelligent Engineering Systems and Computational Cybernetics

ISBN-13: 9781402086779 / Angielski / Twarda / 2009 / 460 str.

J. A. Tenreiro Machado; Bela Patkai; Imre J. Rudas

Opis

Engineering practice often has to deal with complex systems of multiple variable and multiple parameter models almost always with strong non-linear coupling. The conventional analytical techniques-based approaches for describing and predicting the behaviour of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. These approaches normally are too categorical in the sense that in the name of modelling accuracy they try to describe all the structural details of the real physical system to be modelled. This can significantly increase the intricacy of the model and may result in a enormous computational burden without achieving considerable improvement of the solution. The best paradigm exemplifying this situation may be the classic perturbation theory: the less significant the achievable correction, the more work has to be invested to obtain it. A further important component of machine intelligence is a kind of structural uniformity giving room and possibility to model arbitrary particular details a priori not specified and unknown. This idea is similar to the ready-to-wear industry, which introduced products, which can be slightly modified later on in contrast to tailor-made creations aiming at maximum accuracy from the beginning. These subsequent corrections can be carried out by machines automatically. This learning ability is a key element of machine intelligence. The past decade confirmed that the view of typical components of the present soft computing as fuzzy logic, neural computing, evolutionary computation and probabilistic reasoning are of complementary nature and that the best results can be applied by their combined application. Today, the two complementary branches of Machine Intelligence, that is, Artificial Intelligence and Computational Intelligence serve as the basis of Intelligent Engineering Systems. The huge number of scientific results published in Journal and conference proceedings worldwide substantiates this statement. The present book contains several articles taking different viewpoints in the field of intelligent systems. "

Spis treści

Part I Intelligent Robotics; On-Line Trajectory Time-Scaling to Reduce Tracking Error, by Emese Szádeczky-Kardoss, Bálint Kiss; Intelligent Mobile Robot Control in Unknown Environments, by Gyula Mester; Local and Remote Laboratories in the Education of Robot Architectures, by Istvan Matijevics; Force–Impedance Control of a 6-dof Parallel Manipulator, by António M. Lopes, Fernando G. Almeida; Robotic manipulators with vibrations: Short time Fourier transform of fractional spectra, by Miguel F.M. Lima, J.A. Tenreiro Machado, Manuel Crisóstomo; Part II Artificial Intelligence; Classifying Membrane Proteins in the Proteome by Using Artificial Neural Networks Based on the Preferential Parameters of Amino Acids, by Subrata K Bose, Antony Browne, Hassan Kazemian, Kenneth White; Multi-Channel Complex Non-linear Microstatistic Filters. Structure and Design, by Dušan Kocur, Jozef Kraj?nák, Stanislav Marchevský; Extracting and Exploiting Linguistic Information from a Fuzzy Process Model for Fed-Batch Fermentation Control, by Andri Riid, Ennu Rüstern; Part III Computational Intelligence; Transformations and Selection Methods in Document Clustering, by Kristóf Csorba, István Vajk; F-Logic Data and Knowledge Reasoning in the SemanticWeb Context, by Ana Meštrovi´c, Mirko ?Cubrilo; Study on Knowledge and Decision Making, by Dana Klimešová; CNMO: Towards the Construction of a Communication Network Modelling Ontology, by Muhammad Azizur Rahman, Algirdas Pakstas, Frank Zhigang Wang; Computational Intelligence Approach to Condition Monitoring:Incremental Learning and its Application, by Christina B. Vilakazi, Tshilidzi Marwala; Estimating Long-Range Dependent Self-similar Network Traffic: Performance of HEAF(2), by Karim Mohammed Rezaul, Vic Grout; An Approach for Characterising Heavy-tailed Internet Traffic Based on EDF Statistics, by Karim Mohammed Rezaul, Vic Grout; Capturing the Meaning of Internet Search Queries by Taxonomy Mapping, byDomonkos Tikk, Zsolt T. Kardkovács, Zoltán Bánsághi; Scheduling Jobs with Genetic Algorithms, by António Ferrolho, Manuel Crisóstomo; Self-referential reasoning in the light of extended truth qualification principle, by Mohammad Reza Rajati, Hamid Khaloozadeh, Alireza Fatehi; Part IV Intelligent Mechatronics; Control of Differential Mode Harmonic Drive Systems, by László Lemmer, Bálint Kiss; Intelligent Control of an Inverted Pendulum, by Webjorn Rekdalsbakken; Tuning and Application of Integer and Fractional Order PID Controllers, by Ramiro S. Barbosa, Manuel F. Silva, J.A. Tenreiro Machado; Fractional Describing Function of Systems with Nonlinear Friction, by Fernando B.M. Duarte, J.A. Tenreiro Machado; Generalized Geometric Error Correction in Coordinate Measurement, by Gyula Hermann; Part V Systems Engineering; Fixed Point Transformations Based Iterative Control of a Polymerization Reaction, by József K. Tar, Imre J. Rudas; Reasoning in SemanticWeb Services, by Igor Toujilov, Sylvia Nagl; Defining Requirements and Applying Information Modeling for Protecting Enterprise Assets, by Stephen C. Fortier, Jennifer H. Volk; Investigating the Relationship between Complex Systematic Concepts, by Mohamed H. Al-Kuwaiti, Nicholas Kyriakopoulos; Particle Swarm Design of Digital Circuits, by Cecília Reis and J.A. Tenreiro Machado; From Cybernetics to Plectics: a Practical Approach to Systems Enquiry in Engineering, by Béla Pátkai, József K. Tar, Imre J. Rudas; Part VI Mathematical Methods and Models; Extending the Spatial RelationalModel PLA to Represent Trees, by Ágnes B. Novák, Zsolt Tuza; Two CPG Models for Bipeds: a Brief Review, by Carla M.A. Pinto; Quantity competition in a differentiated duopoly, by Fernanda A. Ferreira, Flávio Ferreira, Miguel Ferreira and Alberto A. Pinto; On the fractional order control of heat systems, by Isabel S. Jesus, J.A. Tenreiro Machado, Ramiro S. Barbosa; Restricting Factors at

Blurb

Engineering practice often has to deal with complex systems of multiple variable and multiple parameter models almost always with strong non-linear coupling. The conventional analytical techniques-based approaches for describing and predicting the behaviour of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. The best paradigm exemplifying this situation may be the classic perturbation theory: the less significant the achievable correction, the more work has to be invested to obtain it.

A further important component of machine intelligence is a kind of “structural uniformity” giving room and possibility to model arbitrary particular details a priori not specified and unknown. This idea is similar to the ready-to-wear industry, which introduced products, which can be slightly modified later on in contrast to tailor-made creations aiming at maximum accuracy from the beginning. These subsequent corrections can be carried out by machines automatically. This “learning ability” is a key element of machine intelligence.

The past decade confirmed that the view of typical components of the present soft computing as fuzzy logic, neural computing, evolutionary computation and probabilistic reasoning are of complementary nature and that the best results can be applied by their combined application.

Today, the two complementary branches of Machine Intelligence, that is, Artificial Intelligence and Computational Intelligence serve as the basis of Intelligent Engineering Systems. The huge number of scientific results published in Journal and conference proceedings worldwide substantiates this statement. The present book contains several articles taking different viewpoints in the field of intelligent systems.