A fuzzy neural network for knowledge acquisition in complex time series

A novel fuzzy neural network, called FuNN, is applied here for time-series modelling. FuNN models have several features that make them well suited to a wide range of knowledge engineering applications. These strengths include fast anti accurate learning, good generalisation capabilities, excellent explanation facilities in the form of semantically:ally meaningful fuzzy rules, and the ability to accommodate 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 anti 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.