Intelligent Engineering Systems and Computational Cybernetics [electronic resource] /
Machado, J.A. Tenreiro.editor.
Pátkai, Béla.editor.
Rudas, Imre J.editor.
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.
Description based upon print version of record.
Intelligent Robotics -- On-Line Trajectory Time-Scaling to Reduce Tracking Error -- Intelligent Mobile Robot Control in Unknown Environments -- Local and Remote Laboratories in the Education of Robot Architectures -- Force–Impedance Control of a Six-dof Parallel Manipulator -- Robotic Manipulators with Vibrations: Short Time Fourier Transform of Fractional Spectra -- Artificial Intelligence -- Classifying Membrane Proteins in the Proteome by Using Artificial Neural Networks Based on the Preferential Parameters of Amino Acids -- Multi-Channel Complex Non-linear Microstatistic Filters: Structure and Design -- Legal Ontologies and Loopholes in the Law -- Computational Intelligence -- Extracting and Exploiting Linguistic Information from a Fuzzy Process Model for Fed-Batch Fermentation Control -- Transformations and Selection Methods in Document Clustering -- F-Logic Data and Knowledge Reasoning in the Semantic Web Context -- Study on Knowledge and Decision Making -- CNMO: Towards the Construction of a Communication Network Modelling Ontology -- Computational Intelligence Approach to Condition Monitoring: Incremental Learning and Its Application -- An Approach for Characterising Heavy-Tailed Internet Traffic Based on EDF Statistics -- Capturing the Meaning of Internet Search Queries by Taxonomy Mapping -- Scheduling Jobs with Genetic Algorithms -- Self-Referential Reasoning in the Light of Extended Truth Qualification Principle -- Intelligent Mechatronics -- Control of Differential Mode Harmonic Drive Systems -- Intelligent Control of an Inverted Pendulum -- Tuning and Application of Integer and Fractional Order PID Controllers -- Fractional Describing Function of Systems with Nonlinear Friction -- Generalized Geometric Error Correction in Coordinate Measurement -- Systems Engineering -- Fixed Point Transformations Based Iterative Control of a Polymerization Reaction -- Reasoning in Semantic Web Services -- Defining Requirements and Applying Information Modeling for Protecting Enterprise Assets -- Investigating the Relationship Between Complex Systematic Concepts -- Particle Swarm Design of Digital Circuits -- From Cybernetics to Plectics: A Practical Approach to Systems Enquiry in Engineering -- Mathematical Methods and Models -- Extending the Spatial Relational Model PLA to Represent Trees -- Quantity Competition in a Differentiated Duopoly -- On the Fractional Order Control of Heat Systems -- Restricting Factors at Modification of Parameters of Associative Engineering Objects -- Flexibility in Stackelberg Leadership -- Investing to Survive in a Duopoly Model -- Stochasticity Favoring the Effects of the R&D Strategies of the Firms -- New Methods and Approaches -- Defining Fuzzy Measures: A Comparative Study with Genetic and Gradient Descent Algorithms -- A Quantum Theory Based Medium Access Control for Wireless Networks -- A Concept for Optimizing Behavioural Effectiveness & Efficiency.
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.
Computer science.
Engineering.
Artificial intelligence.
Industrial engineering.
Production engineering.
System theory.
Computer Science, general.
Engineering, general.
Artificial Intelligence.
Artificial Intelligence.
Industrial and Production Engineering.
Systems Theory, Control.