نام کتاب | Fuzzy Networks for Complex Systems: A Modular Rule Base Approach |

ISBN | 3642155995 |

نويسنده | Alexander Gegov |

ناشر | Springer |

سال انتشار | 2010 |

تعداد صفحات | 298 |

اندازه فايل | 2.3 |

فرمت کتاب | |

لينک دانلود | برای مشاهده لینک دانلود لطفاً وارد سایت شوید |

This book introduces the novel concept of a fuzzy network whose nodes are rule bases and the connections between the nodes are the interactions between the rule bases in the form of outputs fed as inputs. The concept is presented as a systematic study for improving the feasibility and transparency of fuzzy models by means of modular rule bases whereby the model accuracy and efficiency can be optimised in a flexible way. The study uses an effective approach for fuzzy rule based modelling of complex systems that are characterised by attributes such as nonlinearity, uncertainty, dimensionality and structure.The approach is illustrated by formal models for fuzzy networks, basic and advanced operations on network nodes, properties of operations, feedforward and feedback fuzzy networks as well as evaluation of fuzzy networks. The results are demonstrated by numerous examples, two case studies and software programmes within the Matlab environment that implement some of the theoretical methods from the book. The book shows the novel concept of a fuzzy network with networked rule bases as a bridge between the existing concepts of a standard fuzzy system with a single rule base and a hierarchical fuzzy system with multiple rule bases.

### Review

From the reviews:

“The reader is led through the processes in such a way that the next step can almost be anticipated, but is still informative. The reader is led through, for example, a single feedback system then drawn into multiple feedback loops. Of course having dealt with amalgamation of multiple nodes in the earlier chapters, multiple feedback loops hold no fears as the overall strategy isnow clear.” (Chris J. Hinde, Fuzzy Sets and Systems, Vol. 225, 2013)

“We get a complete description of the approach including the formal underpinning, practical examples, case studies and Matlab code to implement aspects of fuzzy networks. … I would like to see the approach used in extremely complex problems and researchers who adopt this approach have enough detail to do that in this book. … provides a comprehensive introduction to fuzzy networks from an eminent researcher in the field. If you are interested in exploring large complex fuzzy systems it is well worth a read.” (Robert John, IEEE Computational Intelligence Magazine, February, 2012)

“The book contain the main motivations for using fuzzy techniques and fuzzy networks, and a formal description of the corresponding fuzzy networks. … will be rewarded with knowledge of new innovative tools, tools which are extremely promising in the challenging area of the analysis of real-life complex systems.” (Vladik Kreinovich, Journal of Intelligent & Fuzzy Systems, Vol. 23, 2012)

### From the Back Cover

This book introduces the novel concept of a fuzzy network whose nodes are rule bases and the connections between the nodes are the interactions between the rule bases in the form of outputs fed as inputs. The concept is presented as a systematic study for improving the feasibility and transparency of fuzzy models by means of modular rule bases whereby the model accuracy and efficiency can be optimised in a flexible way. The study uses an effective approach for fuzzy rule based modelling of complex systems that are characterised by attributes such as nonlinearity, uncertainty, dimensionality and structure.The approach is illustrated by formal models for fuzzy networks, basic and advanced operations on network nodes, properties of operations, feedforward and feedback fuzzy networks as well as evaluation of fuzzy networks. The results are demonstrated by numerous examples, two case studies and software programmes within the Matlab environment that implement some of the theoretical methods from the book. The book shows the novel concept of a fuzzy network with networked rule bases as a bridge between the existing concepts of a standard fuzzy system with a single rule base and a hierarchical fuzzy system with multiple rule bases.