and the dependence of y on x into a Taylor series and neglect quadratic and higher order terms in this expansion. Thus, we have a linear dependence y = Ax + b (for some matrix A and vector b), and we get a linear equation that we have to solve in order to reconstruct x: Ax = c; where c = y \Gamma b, y is measured, A and b are known, and x has to be determined. For example, when we reconstruct how a single x depends on time x = x(t), we get a linear integral equation Z A(t; t 0 ) \Delta x(t 0 ) dt =<
The purpose of this book is to present a methodology for designing and tuning fuzzy expert systems i...
We show that fuzzy logic and other soft computing approaches explain and justify heuristic numerical...
This book examines fuzzy relational calculus theory with applications in various engineering subject...
Abstract In this paper, we show that fuzzy theory can explain heuristic methods in inverse problems ...
In this paper, we show that fuzzy theory can explain heuristic methods in inverse problems and numer...
Abstract. In many applied problems (geophysics, medicine, astronomy, etc) we cannot directly measure...
Calculating the matrix inverse is a key point in solving linear equation system, which involves comp...
In practice, there is often a need to describe the relation y = f(x) between two quantities in algor...
Abstract. Linear systems of equations, with uncertainty on the parameters, play a major role in vari...
Most applications of fuzzy techniques use piece-wise linear (triangular or trapezoid) membership fun...
Detecting the beginning and the end of the business cycle is an important and difficult economic pro...
In many practical applications, we need to process data -- e.g., to predict the future values of dif...
We show that fuzzy logic and other soft computing approaches explain and justify heuristic numerical...
Maximum entropy method and its heuristic generalizations are very useful in image processing. In thi...
In signal and image processing, it is often beneficial to use semi-heuristic Lp-methods, i.e., metho...
The purpose of this book is to present a methodology for designing and tuning fuzzy expert systems i...
We show that fuzzy logic and other soft computing approaches explain and justify heuristic numerical...
This book examines fuzzy relational calculus theory with applications in various engineering subject...
Abstract In this paper, we show that fuzzy theory can explain heuristic methods in inverse problems ...
In this paper, we show that fuzzy theory can explain heuristic methods in inverse problems and numer...
Abstract. In many applied problems (geophysics, medicine, astronomy, etc) we cannot directly measure...
Calculating the matrix inverse is a key point in solving linear equation system, which involves comp...
In practice, there is often a need to describe the relation y = f(x) between two quantities in algor...
Abstract. Linear systems of equations, with uncertainty on the parameters, play a major role in vari...
Most applications of fuzzy techniques use piece-wise linear (triangular or trapezoid) membership fun...
Detecting the beginning and the end of the business cycle is an important and difficult economic pro...
In many practical applications, we need to process data -- e.g., to predict the future values of dif...
We show that fuzzy logic and other soft computing approaches explain and justify heuristic numerical...
Maximum entropy method and its heuristic generalizations are very useful in image processing. In thi...
In signal and image processing, it is often beneficial to use semi-heuristic Lp-methods, i.e., metho...
The purpose of this book is to present a methodology for designing and tuning fuzzy expert systems i...
We show that fuzzy logic and other soft computing approaches explain and justify heuristic numerical...
This book examines fuzzy relational calculus theory with applications in various engineering subject...