4 edition of **Soft Computing for Control of Non-Linear Dynamical Systems (Studies in Fuzziness and Soft Computing)** found in the catalog.

- 106 Want to read
- 21 Currently reading

Published
**March 1, 2001**
by Physica-Verlag Heidelberg
.

Written in English

- Artificial intelligence,
- Automatic control engineering,
- Engineering measurement & calibration,
- Fuzzy set theory,
- Soft computing,
- Computers - General Information,
- Theory Of Computing,
- Computers,
- Linear Programming,
- Computer Books: General,
- Engineering - Electrical & Electronic,
- Control Theory,
- Artificial Intelligence - General,
- Computers / Artificial Intelligence,
- Evolutionary Computation,
- Fuzzy Logic,
- Fuzzy-Logik,
- Hybrid Intelligent Systems,
- Intelligent Control,
- Advanced,
- Computer Science,
- Dynamics

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 224 |

ID Numbers | |

Open Library | OL9692832M |

ISBN 10 | 3790813494 |

ISBN 10 | 9783790813494 |

Get this from a library! Modelling, simulation and control of non-linear dynamical systems: an intelligent approach using soft computing and fractal theory. [Patricia Melin; Oscar Castillo]. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract. We describe in this paper the application of soft computing techniques to controlling non-linear dynamical systems in realworld problems. Soft computing consists of fuzzy logic, neural networks, evolutionary computation, and chaos theory. Controlling realworld non-linear dynamical systems may require the use.

CNNs' computation-based systems represent new opportunities for improving the soft-computation toolbox. The application of soft computing to complex systems and in particular to chaotic systems with the generation of chaotic dynamics by using CNN is also described. This course provides an introduction to nonlinear deterministic dynamical systems. Topics covered include: nonlinear ordinary differential equations; planar autonomous systems; fundamental theory: Picard iteration, contraction mapping theorem, and Bellman-Gronwall lemma; stability of equilibria by Lyapunov's first and second methods; feedback linearization; and application to nonlinear.

The course addresses dynamic systems, i.e., systems that evolve with time. Typically these systems have inputs and outputs; it is of interest to understand how the input affects the output (or, vice-versa, what inputs should be given to generate a desired output). In particular, we will concentrate on systems that can be modeled by Ordinary Differential Equations (ODEs), and that satisfy. Controlling realworld non-linear dynamical systems may require the use of several soft computing techniques to achieve the desired performance in practice. For this reason, several hybrid intelligent architectures have been developed. The basic idea of these hybrid architectures is to combine the advantages of each of the techniques involved in.

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The book describes the application of soft computing techniques to modelling, simulation and control of non-linear dynamical systems.

Hybrid intelligence systems, which integrate different techniques and mathematical models, are also presented. This book presents a unified view of modelling, simulation, and control of non linear dynamical systems using soft computing techniques and fractal theory.

Our particular point of view is that modelling, simulation, and control are problems that cannot be considered apart, because they are intrinsically related in real world by: Modelling, Simulation and Control of Non-linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory (Numerical Insights Book 2) - Kindle edition by Melin, Patricia, Castillo, Oscar.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Modelling, Simulation and Control Manufacturer: CRC Press.

This book presents a unified view of modelling, simulation, and control of non linear dynamical systems using soft computing techniques and fractal theory. Our particular point of view is that modelling, simulation, and control are problems that cannot be considered apart, because they are intrinsically related in real world applications.

Soft Computing for Control of Non-Linear Dynamical Systems. Controlling real-world non-linear dynamical systems may require the use of several soft computing techniques to achieve the desired performance in practice.

For this reason, several hybrid. Book Description. These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems.

First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems. Written for practicing engineers and advanced students, this book discusses the modeling, simulation, and control of nonlinear dynamic systems using soft computing methods and fractal theory.

Topics covered include fuzzy logic and neural networks, adaptive model-based control, and automated mathematical modeling and simulation. We describe in this book, new methods for modelling, simulation, and control of dynamical systems using soft computing techniques and fractal theory.

Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems. These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems.

First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented. It is illustrated with examples on the PROLOG programming la. Soft Numerical Computing in Uncertain Dynamic Systems is intended for system specialists interested in dynamic systems that operate at different time scales.

The book discusses several types of errors and their propagation, covering numerical methods—including convergence and consistence properties and characteristics—and proving of related theorems within the setting of soft computing.

These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems.

First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems Cited by: Modelling, Simulation and Control of Non-linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory - Kindle edition by Melin, Patricia, Castillo, Oscar.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Modelling, Simulation and Control of Non-linear Dynamical. Chaotic behavior in non-linear dynamical systems is very difficult to detect and control.

Part of the problem is that mathematical results for chaos identification are difficult to use in many cases, and even if one could use them there is an underlying uncertainty in the accuracy of the numerical simulations of the dynamical systems.

The traditional models of distributed computing systems employ the mathematical formalisms of discrete event dynamical systems along with Petri Nets. However, such models are complex to analyze and computationally expensive.

Interestingly, the evolving distributed computing systems closely resemble the discrete dynamical systems. Castillo O., Melin P. () Soft Computing Models for Intelligent Control of Non-linear Dynamical Systems.

In: Mitkowski W., Kacprzyk J. (eds) Modelling Dynamics in Processes and Systems. Studies in Computational Intelligence, vol Buy Soft Computing for Control of Non-Linear Dynamical Systems: 63 (Studies in Fuzziness and Soft Computing) by Castillo, Oscar, Melin, Patricia (ISBN: ) from Amazon's Book Store.

Everyday low prices and free delivery on eligible orders. There may exist an updated edition, book displays a very rich bibliography. The book shows strong connections of the subject matter with optimization, dynamical systems as well as the classical.

We describe in this book, new methods for intelligent manufacturing using soft computing techniques and fractal theory. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems.

We describe in this paper the application of soft computing techniques and fractal theory to the control of non-linear dynamical systems [8]. Soft computing consists of fuzzy logic, neural. Description. The International Journal of System Dynamics Applications (IJSDA) publishes original scientific and quality research on the theory of and advances in dynamical systems with analyses of measure-theoretical and topological aspects.

This interdisciplinary journal provides audiences with an extensive exploration of the perspectives and methods of system dynamics and system thinking. Neural networks have been applied very successfully in the identification and control of dynamic systems.

The universal approximation capabilities of the Multi-Layer Perceptron (MLP) have made it a popular choice for modeling non-linear systems and for implementing general-purpose non-linear controllers.Modelling, Simulation and Control of Non-linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory (Numerical Insights) Oct .