We introduce a concept of self-organizing Hybrid Neurofuzzy Networks (HNFN), a hybrid modeling architecture combining neurofuzzy (NF) and polynomial neural networks(PNN). The development of the Self-organizing HNFN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the Self-organizing HNFN results from a...

A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This net...

In this paper, a neuro-fuzzy system identification using measured input and output data are carried out. A model-free learning from “examples” methodology is developed to train a neuro-fuzzy model of a smallsize helicopter. The helicopter model is obtained and tuned using training data gathered while a teacher operates the helicopter. Behavior-based model architecture is used, with each behavio...

Atualmente, a utilização de sistemas inteligentes híbridos que utilizam combinação técnicas inteligência artificial como, por exemplo, redes neurais e lógica fuzzy, tem-se tornado comuns na elaboração modelos complexos simulação estimar parâmetros desejados. O objetivo deste estudo foi desenvolver inferência adaptativos neurofuzzy (ANFIS) para predizer produção do abacaxizeiro ‘Vitória’ realiza...

This work explores the potential of Bspline based Adaptive NeuroFuzzy Wavelet control to damp low frequency power system oscillations using Static Synchronous Series Compensator (SSSC). A comparison of direct and indirect adaptive control based on Hybrid Adaptive Bspline Wavelet Control (ABSWC) is presented by introducing the online identification block. ABSWC with Identification (ABSWCI) provi...

Adaptability in non-stationary contexts is a very important property and a constant desire for modern intelligent systems and is usually associated with dynamic system behaviors. In this framework, we present a novel methodology of dynamic resource control and optimization for neurofuzzy inference systems. Our approach involves a neurofuzzy model with structural learning capabilities that adds ...