Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy infer...

Solar and Wind energy based Renewable Energy Systems (RES) are one of the most rapidly growing technologies as a means producing clean electrical energy. Grid integration RES involves various types power electronics-based converters inverters. These electronic devices produce harmonics at their terminals, which transferred to grid. Harmonic forecasting is techniques used design harmonic mitigat...

In this study, the road traffic congestion of Dehradun city is evaluated from traffic flow information using fuzzy techniques. Three different approaches namely Sugeno, Mamdani models which are manually tuned techniques, and an Adaptive Neuo-Fuzzy Inference System (ANFIS) which an automated model decides the ranges and parameters of the membership functions using grid partition technique, based...

An optimal data partitioning in parallel/distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster merging. Though partitioning using Kd-Tree is being conventionally used in academia, it suffers from performance drenches and bias (non equal distribution) as dimen...

OBJECTIVES In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. METHODS A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. ...

This paper presents the design and preliminary evaluation of hierarchical partitioning and load-balancing techniques for distributed Structured Adaptive Mesh Refinement (SAMR) applications. The overall goal of these techniques is to enable the load distribution to reflect the state of the adaptive grid hierarchy and exploit it to reduce synchronization requirements, improve load-balance, and en...

This paper describes a new class of neuro-fuzzy models, called Reinforcement Learning Hierarchical NeuroFuzzy Systems (RL-HNF). These models employ the BSP (Binary Space Partitioning) and Politree partitioning of the input space [Chrysanthou,1992] and have been developed in order to bypass traditional drawbacks of neuro-fuzzy systems: the reduced number of allowed inputs and the poor capacity t...

This paper is aimed to propose a grid density clustering algorithm for spatio-temporal data that is based on the adaptation of the grid density based clustering algorithm. The algorithm is based on AGRID+ algorithm with 7 steps: partitioning, computing distance threshold, calculating densities, compensating densities, calculating density threshold (DT), clustering and removing noises. The adapt...

Cluster and grid computing has made hierarchical and heterogeneous computing systems increasingly common as target environments for large-scale scientific computation. A cluster may consist of a network of multiprocessors. A grid computation may involve communication across slow interfaces. Modern supercomputers are often large clusters with hierarchical network structures. For maximum efficien...