In this paper a novel neural fuzzy inference network (NFIN) it is proposed. The NFIN represent a modified Takagi-Sugeno-Kang (TSK) type fuzzy rule based model with neural network learning ability. The rules in the NFIN are created and adapted in an on-line learning algorithm. The structure learning together with the parameter learning forms the learning algorithms for the neural fuzzy network. ...

In this paper we propose a dynamic evolving neuro-fuzzy inference system (DENFIS) to forecast mortality. DENFIS is an adaptive intelligent system suitable for dynamic time series prediction. An Evolving Cluster Method (ECM) drives the learning process. The typical fuzzy rules of the neurofuzzy systems are updated during the learning process and adjusted according to the features of the data. Th...

This paper addresses the issue of controlling nonlinear processes by the use of the nonlinear model predictive control formulation. To handle the nonlinearities, a neuro-fuzzy process model is suggested as a means to model processes with strong nonlinearities depending on the operating region. In this paper the neurofuzzy approach is used for the modelling and control of a strongly nonlinear pH...

This paper reports application of neurofuzzy inference system (NFIS) and self organizing feature map neural networks (SOM) on detection of contact state in a block system. In this manner, on a simple system, the evolution of contact states, by parallelization of DDA, has been investigated. So, a comparison between NFIS and SOM results has been presented. The results show applicability of the pr...

The article is devoted to the issue of creating a mathematical model problem making management decisions in electric power facilities based on modern intelligent technologies, which makes it possible take into account influence various factors operating modes system. A systematic approach describing processes language theory fuzzy sets proposed. To solve controlling system, neurofuzzy network h...

To realize or implement the large dimensional image data, it may be taking a longer search time to detect the desired target. Recently, for the large amount of data and information in engineering or biomedical applications, various techniques including soft-computing techniques such as neural networks, fuzzy logic, or genetic algorithms, and multivariate analysis techniques like factor analysis...

Neural networks and Neurofuzzy models have been successfully used in the prediction of nonlinear time series. Several learning methods have been introduced to train the Neurofuzzy predictors, such as ANFIS, ASMOD and FUREGA. Many of these methods, constructed over Takagi Sugeno fuzzy inference system, are characterized by high generalization. However, they differ in computational complexity. Th...