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Kalaivani, S.
- Parameter Estimation of Valve Stiction Using Ant Colony Optimization
Abstract Views :142 |
PDF Views:0
Authors
S. Kalaivani
1,
D. Karthiga
2
Affiliations
1 Department of Electrical and Instrumentation Engineering, Muthayammal Engineering College, IN
2 Department of Electrical and Instrumentation Engineering, Annamalai University, IN
1 Department of Electrical and Instrumentation Engineering, Muthayammal Engineering College, IN
2 Department of Electrical and Instrumentation Engineering, Annamalai University, IN
Source
ICTACT Journal on Soft Computing, Vol 2, No 4 (2012), Pagination: 371-376Abstract
In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most severe problem in the control valves. Thus the measurement data from an oscillating control loop can be used as a possible diagnostic signal to provide an estimate of the stiction magnitude. Quantification of control valve stiction is still a challenging issue. Prior to doing stiction detection and quantification, it is necessary to choose a suitable model structure to describe control-valve stiction. To understand the stiction phenomenon, the Stenman model is used.
Ant Colony Optimization (ACO), an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model are estimated using ant colony optimization, from the input-output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.
Keywords
Control Valve Stiction, Nonlinear System Identification, Stenman Model, Ant Colony Optimization.
- Modified Bee Colony with Bacterial Foraging Optimization based Hybrid Feature Selection Technique for Intrusion Detection System Classifier Model
Abstract Views :232 |
PDF Views:0
Authors
S. Kalaivani
1,
G. Gopinath
1
Affiliations
1 School of Computer Science and Engineering, Bharathidasan University, IN
1 School of Computer Science and Engineering, Bharathidasan University, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 4 (2020), Pagination: 2146-2152Abstract
Feature selection (FS) plays an essential role in creating machine learning models. The unrelated characteristics of the data disturb the precision of the perfection and upsurges the training time required to build the model. FS is a significant process in creating the Intrusion Detection System (IDS). In this document, we propose a technique for selecting container functions for IDS. To develop the performance capacity of the modified Artificial Bee Colony (ABC) procedure, a hybrid method is presented in which the swarm behavior of the Bacterial Foraging Optimization (BFO) algorithm is entered into the Modified Bee Colony (MBC) procedure to perform a local search. The proposed Hybrid MBC-BFO algorithm is analyzed with three different classification techniques which are separately analyzed to verify the proposed performance. The classification techniques are Artificial Neural Networks (ANN), Recursive Neural Network (ReNN), and Recurrent Neural Network (RNNs). The proposed algorithm has passed several algorithms for selecting advanced functions in terms of detection accuracy, recall, precision, false positive rate, and F-score.Keywords
Swarm Intelligence, Modified Bee Colony, Bacterial Foraging Optimization, Feature Selection, IDS, KDDCUP’99.References
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- S. Kalaivani and Gopinath Ganapath, “Bacterial Foraging Optimization for Enhancing the Security in Intrusion Detection System”, International Journal of Scientific and Technology Research, Vol. 9, No. 2, pp. 1-8, 2020.