The aim of the paper is to introduce a concurrent fuzzy neural network approach, representing a winner-takes-all collection of fuzzy Gaussian modules. Our proposed model will be applied for the pattern classification. The fuzzy neural model consists of a set of M fuzzy neural networks, one for every class, each network having a single output. The output value corresponding to the M k k , 1 ,  ...

It has been well established that neural networks provide a reasonable and powerful alternative to conventional classifiers. During the past few years there has been a large and energetic upswing in research efforts aimed at synthesizing fuzzy logic with neural networks. This combination of fuzzy logic and neural networks seems natural because two approaches generally attack the design of “inte...

In this paper modifications of intensifiers have been done for the injection into the receptive field of the fuzzy neural networks. Algorithmic developments for these modifiers are carried out for single-input single-output (SISO) as well as for multi-input multi-output (MIMO) fuzzy neural networks. This work can be beneficial for applications in different fields such as image processing, patte...

optimization of machining parameters is very important and the main goal in every machining process. surface finishing prediction is a pre-requirement to establish a center for automatic machining operations. in this research, a neuro-fuzzy approach is used in order to model and predict the surface roughness in dry turning. this approach has both the learning capability of neural network and li...

In this paper, some application of computational intelligence techniques in active networking technology well be presented. Computational intelligent techniques, e.g., neural networks, fuzzy systems, neuro-fuzzy systems, and evolutionary algorithms have been successfully applied for many engineering problems. The introduction of active networking adds a high degree of flexibility in customizing...

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets...

daily constant discharges are needed estimating daily discharge in the hydrological model. the different number of statistical years, statistical deficiencies, and measurement error leads to the formation of time series with an uncommon time base. hence the reconstruction of daily discharge data is of paramount importance. in this research, daily discharge was reconstructed in two stages in one...