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...

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized ...

The problem of finding relations between structure of large molecules and their chemical and biological activity is known as the structure-activity relation problem (SAR). Two neural networks developed in our group were applied to this problem: the Feature Space Mapping neurofuzzy system and the constrained MLP network used to extract logical rules. Two SAR data sets were analyzed: antibiotic a...

Multidimensional scaling is one of the important techniques for a big data management. In this paper, various statistical analyses are compared to find the best-fitting method for a representation of a higher dimensional data using the reduced or smaller dimensional data using various multivariate analyses with maximum likelihood estimation through the neurofuzzy systems, which estimate the pre...

A framework of new unified neural and neuro-fuzzy approaches for integrating implicit and explicit knowledge in neuro-symbolic systems is proposed. In the developed hybrid system, training data set is used for building neurofuzzy modules, and represents implicit domain knowledge. On the other hand, the explicit domain knowledge is represented by fuzzy rules, which are directly mapped into equiv...

Solution of inverse kinematic equations is complex problem, the complexity comes from the nonlinearity of joint space and Cartesian space mapping and having multiple solution. In this work, four adaptive neurofuzzy networks ANFIS are implemented to solve the inverse kinematics of 4-DOF SCARA manipulator. The implementation of ANFIS is easy, and the simulation of it shows that it is very fast an...