6,131 research outputs found
ANFIS Modeling of Dynamic Load Balancing in LTE
Modelling of ill-defined or unpredictable systems can be very challenging. Most models have relied on
conventional mathematical models which does not adequately track some of the multifaceted challenges
of such a system. Load balancing, which is a self-optimization operation of Self-Organizing Networks
(SON), aims at ensuring an equitable distribution of users in the network. This translates into better user
satisfaction and a more efficient use of network resources. Several methods for load balancing have been
proposed. While some of them have a very buoyant theoretical basis, they are not practical. Furthermore,
most of the techniques proposed the use of an iterative algorithm, which in itself is not computationally
efficient as it does not take the unpredictable fluctuation of network load into consideration. This chapter
proposes the use of soft computing, precisely Adaptive Neuro-Fuzzy Inference System (ANFIS) model,
for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of
neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost
effective and closer to human intuition. Three key load parameters (number of satisfied user in the net-
work, virtual load of the serving eNodeB, and the overall state of the target eNodeB) are used to adjust
the hysteresis value for load balancing
VHDL-AMS based genetic optimization of a fuzzy logic controller for automotive active suspension systems
This paper presents a new type of fuzzy logic controller (FLC) membership functions for automotive active suspension systems. The shapes of the membership functions are irregular and optimized using a genetic algorithm (GA). In this optimization technique, VHDL-AMS is used not only for the modeling and simulation of the fuzzy logic controller and its underlying active suspension system but also for the implementation of a parallel GA. Simulation results show that the proposed FLC has superior performance to that of existing FLCs that use triangular or trapezoidal membership functions
A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs
Representing the reservoir as a network of discrete compartments with
neighbor and non-neighbor connections is a fast, yet accurate method for
analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale
compartments with distinct static and dynamic properties is an integral part of
such high-level reservoir analysis. In this work, we present a hybrid framework
specific to reservoir analysis for an automatic detection of clusters in space
using spatial and temporal field data, coupled with a physics-based multiscale
modeling approach. In this work a novel hybrid approach is presented in which
we couple a physics-based non-local modeling framework with data-driven
clustering techniques to provide a fast and accurate multiscale modeling of
compartmentalized reservoirs. This research also adds to the literature by
presenting a comprehensive work on spatio-temporal clustering for reservoir
studies applications that well considers the clustering complexities, the
intrinsic sparse and noisy nature of the data, and the interpretability of the
outcome.
Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal
Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin
BigFCM: Fast, Precise and Scalable FCM on Hadoop
Clustering plays an important role in mining big data both as a modeling
technique and a preprocessing step in many data mining process implementations.
Fuzzy clustering provides more flexibility than non-fuzzy methods by allowing
each data record to belong to more than one cluster to some degree. However, a
serious challenge in fuzzy clustering is the lack of scalability. Massive
datasets in emerging fields such as geosciences, biology and networking do
require parallel and distributed computations with high performance to solve
real-world problems. Although some clustering methods are already improved to
execute on big data platforms, but their execution time is highly increased for
large datasets. In this paper, a scalable Fuzzy C-Means (FCM) clustering named
BigFCM is proposed and designed for the Hadoop distributed data platform. Based
on the map-reduce programming model, it exploits several mechanisms including
an efficient caching design to achieve several orders of magnitude reduction in
execution time. Extensive evaluation over multi-gigabyte datasets shows that
BigFCM is scalable while it preserves the quality of clustering
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