Neurofuzzy adaptive B-spline circuit performance modelling aided by means of genetic algorithm /
BIAŁKO, Michał. Politechnika Koszalińska - Wydział Elektroniki, Katedra Inżynierii Komputerowej 1996 - .
Neurofuzzy adaptive B-spline circuit performance modelling aided by means of genetic algorithm / R. Horbowski, Michał Białko. - 2001.
Dane z Informatora o publikowanych wynikach prac naukowo-badawczych w 2001 roku Wydziału Elektroniki.
An application of a lattice adaptive neurofuzzy system to circuit performance modelling is presented in the paper. The investigated system makes use of the B-spline membership functions and a structure of the system is determined by means of a genetic algorithm. The presented approach possesses good modelling capabilities and, contrary to non-lattice neurofuzzy approaches, can explore structural dependencies existing in training data supporting us with valuable knowledge about the modelled circuit. This knowledge gives an insight into behaviour of the modelled performance function and makes it possible to reduce a size of the set of circuit variables, to simplify the structure of the model and hence to speed-up its evaluation and identification.
Algorytmy genetyczne.
004.021
Neurofuzzy adaptive B-spline circuit performance modelling aided by means of genetic algorithm / R. Horbowski, Michał Białko. - 2001.
Dane z Informatora o publikowanych wynikach prac naukowo-badawczych w 2001 roku Wydziału Elektroniki.
An application of a lattice adaptive neurofuzzy system to circuit performance modelling is presented in the paper. The investigated system makes use of the B-spline membership functions and a structure of the system is determined by means of a genetic algorithm. The presented approach possesses good modelling capabilities and, contrary to non-lattice neurofuzzy approaches, can explore structural dependencies existing in training data supporting us with valuable knowledge about the modelled circuit. This knowledge gives an insight into behaviour of the modelled performance function and makes it possible to reduce a size of the set of circuit variables, to simplify the structure of the model and hence to speed-up its evaluation and identification.
Algorytmy genetyczne.
004.021