Data Engineering: Fuzzy Mathematics in Systems Theory and Data AnalysisAlthough data engineering is a multi-disciplinary field with applications in control, decision theory, and the emerging hot area of bioinformatics, there are no books on the market that make the subject accessible to non-experts. This book fills the gap in the field, offering a clear, user-friendly introduction to the main theoretical and practical tools for analyzing complex systems. An ftp site features the corresponding MATLAB and Mathematical tools and simulations. Market: Researchers in data management, electrical engineering, computer science, and life sciences. |
Contents
| 1 | |
2 Uncertainty Techniques | 31 |
System Identification | 69 |
4 Propositions as Subsets of the Data Space | 83 |
5 Fuzzy Systems and Identification | 109 |
6 RandomSet Modelling and Identification | 129 |
7 Certain Uncertainty | 145 |
8 Fuzzy Inference Engines | 161 |
Other editions - View all
Data Engineering: Fuzzy Mathematics in Systems Theory and Data Analysis Olaf Wolkenhauer No preview available - 2004 |
Common terms and phrases
algebra algorithm analysis antecedent approximate reasoning assumed basis function calculated called concepts confidence consider data set data space defined definition denoted density estimation described dynamic system elements equivalence classes equivalence relation error event example filter find finite first fixed forecast formal model framework function f fuzzy clustering fuzzy controller fuzzy graph fuzzy mathematics fuzzy model fuzzy partition fuzzy PI-controller fuzzy relations fuzzy set fuzzy system fuzzy-c-means given if-then implies inference engine input input-output integral introduced least-squares linear mapping means measure membership functions minimize model structure multi-valued maps negative nonlinear objects observables obtain optimal orthogonal output parameters partition matrix possibility distribution prediction probabilistic probability problem product space propositional calculus propositions quotient set random variables regression regressor risk functional rule rule-based systems sample Section sequence similarity relations specific state-space statistical stochastic process subsets t-norm time-series training data transitivity uncertainty values vector zero
Popular passages
Page xxxii - As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.
Page xx - I may remark parenthetically that the modern apparatus of the theory of small samples, once it goes beyond the determination of its own specially defined parameters and becomes a method for positive statistical inference in new cases, does not inspire me with any confidence, unless it is applied by a statistician by whom the main elements of the dynamics of the situation are either explicitly known or implicitly felt.
Page xx - Wiener16 has made much the same point (p. 35): '. . . the modern apparatus of the theory of small samples, once it goes beyond the determination of its own specially defined parameters and becomes a method for positive statistical inference in new cases, does not inspire one with any confidence, unless it is applied by a statistician by whom the main elements of the dynamics of the situation are either explicitly known or implicitly felt.