This paper proposes an adaptive neurofuzzy interface system ANFIS approach to identify the real power transfer between generators. Based on solved load flow results, it first uses modified nodal equation method MNE to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to train the designed ANFIS. It also incorpor...

This paper proposed a novel adaptive neuro-fuzzy inference system (ANFIS), which combines subtract clustering, employs adaptive Hamacher T-norm and improves the prediction ability of ANFIS. The expression of multiinput Hamacher T-norm and its relative feather has been originally given, which supports the operation of the proposed system. Empirical study has testified that the proposed model ove...

a r t i c l e i n f o Keywords: Subtractive clustering Adaptive network-based fuzzy inference system Technical indicators Adaptive learning Genetic algorithm Technical analysis is one of the useful forecasting methods to predict the future stock prices. For professional stock analysts and fund managers, how to select necessary technical indicators to forecast stock trends is important. Traditio...

In this paper, a comparative study of classification of the analog modulated communication signals using clustering techniques is introduced. Four different clustering algorithms are implemented for classifying the analog signals. These clustering techniques are K-means clustering, fuzzy c-means clustering, mountain clustering and subtractive clustering. Two key features are used for characteri...

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....

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...

 Clustering is a challenging problem in data mining, requiring both accurate determination of the number of clusters and correct clustering of the data. Fuzzy C-means (FCM) is a popular algorithm using the partitioning approach to solve this problem. A drawback to FCM is that it requires the number of clusters to be set a priori. In this study, we combine FCM with Genetic Algorithm (GA), Subtr...

Fuzzy C-means (FCM) is a popular algorithm using the partitioning approach to solve problems in data clustering. A drawback to FCM, however, is that it requires the number of clusters and the clustering partition matrix to be set a priori. Typically, the former is set by the user and the latter is initialized randomly. This approach may cause the algorithm get stuck in a local optimum because F...

The efficiency of a nation’s progress is determined by variety factors; however, transportation plays critical role in boosting because it facilitates trade and communication between countries. majority powered fossil fuels such as gasoline or diesel, which will be depleted less than 50 years. Another option to operate systems after replacing conventional vehicles with electric (EV). Powering t...