## Complexity in Physics and TechnologyA system is loosely defined as complex if it is composed of a large number of elements, interacting with each other, and the emergent global dynamics is qualitatively different from the dynamics of each one of the parts. The global dynamics may be either ordered or chaotic and among the most interesting emergent global properties are those of learning and adaptation.Complex systems, in the above sense, appear in many fields ranging from physics and technology to life and social sciences. Research in complex systems involves therefore a wide range of topics, studied in seemingly disparate fields. This calls for some effort to develop general principles and a common language so that tools developed in one field may be put to use in other fields.By collecting a few surveys of complex systems studies in physics and in technology and emphasizing their common mechanisms and interrelationships, this book attempts to contribute to the development of a common language in the sciences of complexity.Topics covered include: Integrated design in aeronautics; time and space decomposition of complex structures; complexity in electrical power networks; earthquake behaviour of structures; signal processing; fiability; use of unstable orbits in astrodynamics; dynamics of coupled oscillators; fuzziness; dark and bright solitons; neural networks; chaos and parametric perturbations; chaotic fluid dynamics; early vision and image restoration; stochastic processes in automated production lines. |

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### Contents

Discovering Coherent Structures in Nonlinear Spatial Systems | 3 |

Uncertainty Subjectivity Fuzziness | 29 |

The In and Out An Evolutionary Approach | 47 |

VISION | 51 |

Effects of Parametric Perturbation on the Onset of Chaos | 65 |

Motion of a Satellite in an Elliptic Orbit | 91 |

Equipartition Transition and Lyapunov Exponents in Closed Hamiltonian | 113 |

Some Experimental Results on Asymmetric Boltzmann Machines | 151 |

Optimization with Potts Neural Networks | 181 |

Early Vision with Cellular Automata Fields | 195 |

On the Use of the BiOnhogonal Decomposition | 213 |

From Overabundant Complexity | 233 |

Aspects of Complexity in Sleep Analysis | 249 |

Prediction of Time Series | 263 |

Stochastic Dispersive Transport An Excursion from Statistical Physics | 283 |

InformationSpecific Designs of MultiConnected Neural Network Models | 167 |

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algorithm amplitude analysis attractor background pulse Bar rotation beam behaviour bi-orthogonal decomposition binary Boltzmann machines bright solitons cellular automata chaos chaotic chaotic attractor coefficients complex computational concepts connected considered correlations corresponding coupling dark pulse dark solitons defined described deterministic domain dynamical systems effect electromotor energy entropy equipartition transition evolution field filter finite frequency fundamental dark soliton fuzzy Hadamard IEEE initial condition input pulse interaction Kivshar Lett linear Lyapunov exponent matrix mechanist paradigm mode motion neural network neurons NLS equation nonlinear nonlinear optical observed obtained optical fibers optical solitons optimal orbit oscillations output parameter parse tree patterns pendulum perturbation phase Phys pixels possible probability problem propagation random reconstruction seven-segment display signal single-mode optical fiber small-amplitude solution space spatial spherical pendulum stability stochastic subset theory threshold trajectories variables vector velocity vibration visual cost function vulnerability function weak membrane model zero