Fuzzy controller’s design depends mainly on the rule base and membership functions over the controller’s input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechan...

Information extraction from satellite images is a challenging task. This is because of the associated uncertainty arising out of improper capture and subsequent transfer. Fuzzy systems are suitable for such applications because of the fact that these have the ability to capture minute variations in the patterns presented. Fuzzy systems are expert decision making tools that require support from ...

We present a novel clustering approach, that exploits boosting as the primary means of modelling clusters. Typically, boosting is applied in a supervised classification context; here, we move in the less explored unsupervised scenario. Starting from an initial partition, clusters are iteratively re-estimated using the responses of one-vs-all boosted classifiers. Within-cluster homogeneity and s...

A partitional and a fuzzy clustering algorithm are compared in this paper in terms of accuracy, robustness and efficiency. 3D position data extracted from a stereo-vision system have to be clustered to use them in a tracking application in which a particle filter is the kernel of the estimation task. ‘K-Means’ and ‘Subtractive’ algorithms have been modified and enriched with a validation proces...

This project consists of two parts. The first part is a general review of the previous and current research on human face recognition, including initial motivation, approaches, major problems and solutions, etc. The second part propose a new method for learning of radial basis function (RBF) neural networks which is based on subtractive clustering algorithm(SCA) and its application to face reco...

In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale laye...

 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...

In this work, the subtractive mountain clustering algorithm has been adapted to problem of natural languages processing in view construct a chatbot that answers questions posed by user. The implemented version allosws for association set words into clusters. After finding centre every cluster — most relevant word, all others are aggregated according defined metric language realm. All stored inf...

As the information on the Web grows, the need of recommender systems to ease user navigations becomes evident. There exist many approaches of learning for Web usage based recommender systems. In this study, we apply and compare some of the methods of usage pattern discovery, like simple k-means clustering algorithm, fuzzy relational subtractive clustering algorithm, fuzzy mean field annealing c...