A fuzzy neural network for knowledge acquisition in complex time series

• Autorzy: Kasabov N. , Kim J. , Kozma R.
• Języki publikacji: EN

Abstrakty

EN

A novel fuzzy neural network, called FuNN, is applied here for time-series modeling. FuNN models have several features that make them well suited to a wide range of knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the form of semantically meaningful fuzzy rules, and the ability to accomodate both numerical data and existing expert knowledge about the problem under consideration. We investigate the effectiveness of the proposed neuro-fuzzy hybrid architectures for manipulating the future behaviour of nonlinear dynamical systems and interpreting fuzzy if-then rules. A well-known example of Box and Jenkins is used as a benchmark time series in the proposed modelling approach and the other modelling approach. Finally, experimental results and comparisons with the other popular neuro-fuzzy inference system, namely Adaptive Network-based Fuzzy Inference System (ANFIS) are also presented.

Słowa kluczowe

EN

adaptation; computational neural net; fuzzy logic; fuzzy neural net; knowledge acquisition; time-series and dynamical system

PL

logika rozmyta; sieć neuronowa rozmyta; układ dynamiczny

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 1998
• Tom: Vol. 27, no 4
• Strony: 593--611
• Opis fizyczny: Bibliogr. 18 poz.,Rys., wykr.,

Twórcy

autor

Kasabov N.

autor

Kim J.

autor

Kozma R.