## Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic ModelsThis textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available. |

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### Contents

Rationale Motivations Needs Basics | 1 |

Problems | 103 |

Simulation Experiments | 117 |

Problems | 184 |

Problems | 244 |

Simulation Experiments | 253 |

Algorithm | 266 |

Problems | 303 |

Simulation Experiments | 309 |

Problems | 358 |

Problems | 410 |

Simulation Experiments | 419 |

Basic Nonlinear Optimization Methods | 481 |

Mathematical Tools of Soft Computing | 505 |

Selected Abbreviations | 525 |

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### References to this book

Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi ... Te-Ming Huang,Vojislav Kecman,Ivica Kopriva No preview available - 2006 |

Proceedings of the 6th International Conference on ..., Volume 2 Shie-Yui Liong,Kok-Kwang Phoon,Vladan Babovic No preview available - 2004 |