In this paper a new approach to fault diagnosis in an AC motor is introduced. This system combines a neuro-fuzzy system called FasArt (Fuzzy Adaptive System ART based) and the well-known fuzzy k nearest neighbor algorithm. A set of 15 types of non destructive faults has been tested, reaching a high degree of early fault detection and fault type recognition. Moreover, taking into account the neu...

K-Nearest Neighbor Classification (kNNC) makes the classification by getting votes of the k-Nearest Neighbors. Performance of kNNC is depended largely upon the efficient selection of k-Nearest Neighbors. All the attributes describing an instance does not have same importance in selecting the nearest neighbors. In real world, influence of the different attributes on the classification keeps on c...

In this part, you will implement k-Nearest Neighbor (k-NN) algorithm on the 8scenes category dataset of Oliva and Torralba [1]. You are given a total of 800 labeled training images (containing 100 images for each class) and 1888 unlabeled testing images. Figure 1 shows some sample images from the data set. Your task is to analyze the performance of k-NN algorithm in classifying photographs into...

The nearest neighbour (NN) and k-nearest neighbour (kNN) classi cation rules have been widely used in pattern recognition due to its simplicity and good behaviour. Exhaustive nearest neighbour search can become unpractical when facing large training sets, high dimensional data or expensive similarity measures. In the last years a lot of NN search algorithms have been developed to overcome those...

Fast nearest neighbor (NN ) finding has been extensively studied. While some fast NN algorithms using metrics rely on the essential properties of metric spaces, the others using non-metric measures fail for large-size templates. However, in some applications with very large size templates, the best performance is achieved by NN methods based on the dissimilarity measures resulting in a special ...

A data object is broad if it is one of the k-Nearest Neighbors (k-NN) of many data objects. We introduce a new database primitive called Generalized Nearest Neighbor (GNN) to express data broadness. We also develop three strategies to answer GNN queries efficiently for large datasets of multidimensional objects. The R*-Tree based search algorithm generates candidate pages and ranks them based o...

Classification of objects is an important area in a variety of fields and applications. In the presence of full knowledge of the underlying joint distributions, Bayes analysis yields an optimal decision procedure and produces optimal error rates. Many different methods are available to make a decision in those cases where information of the underlying joint distributions is not presented. The k...

In this paper, using high order perturbative series expansion method, the critical exponents of the order parameter and susceptibility in transition from ferromagnetic to disordered phases for 1D quantum Ising model in transverse field, with ferromagnetic nearest neighbor and anti-ferromagnetic next to nearest neighbor interactions, are calculated. It is found that for small value of the frustr...