This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were us...

Abstract Data-driven models can predict, estimate, and monitor any highly nonlinear multi-variable behaviour of high-temperature superconducting (HTS) materials, devices to analyse their characteristics with a very high accuracy in an almost real-time procedure, which is significant figure merit as compared traditional numerical approaches. The electromechanical twisted HTS tapes under differen...

This work presents a method based on an adaptive neuro-fuzzy inference system (ANFIS) for modeling protein secondary structure prediction which aims at acquiring the unknown structure information of target protein directly from its sequence data which is available. The number of input variables and inference rules are commonly too large, sometimes even huge, to make the model building feasible....

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measure...

In this paper, a subtractive relational fuzzy c-medoids clustering approach is discussed to identify web user session clusters from weblogs, based on their browsing behavior. In this approach, the internal arrangement of data along with the density of pairwise dissimilarity values is favored over arbitrary starting estimations of medoids as done in the conventional relational fuzzy c-medoids al...

In this paper, a subtractive clustering fuzzy identification method and a Sugeno-type fuzzy inference system are used to monitor tile defects in tile manufacturing process. The models for the tile defects are identified by using the firing mechanical resistance, water absorption, shrinkage, tile thickness, dry mechanical resistance and tiles temperature as input data, and using the concavity de...

Fuzzy C-means clustering (FCM) is an important technique used in cluster analysis. The standard FCM algorithm calls the centroids to be randomly initialized resulting in the requirement of making estimations from expert users to determine the number of clusters. To overcome these observed limitations of applying the FCM algorithm, an efficient image segmentation model, Hybrid Fuzzy C-means Algo...