Applying particle swarm optimization algorithm for tuning a neuro-fuzzy inference system for sensor monitoring

Oliveira, Mauro Vitor de; Schirru, Roberto

doi:10.1016/j.pnucene.2008.03.007

Cited by 45 publications

(12 citation statements)

References 8 publications

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“…Particle swarm based artificial neuro-fuzzy inference system. Two latter classifiers were combined in order to introduce the fifth tool: the PSO-ANFIS [38]. Namely, when the ANFIS training is finished (Section 3.4), it is passed in several copies (10% of the swarm population) to the PSO-FIS approach.…”

Section: ])mentioning

confidence: 99%

Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis

Zarychta

Badura

Piętka

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract.A study on computer aided diagnosis of posterior cruciate ligaments is presented in this paper. The diagnosis relies on T1-weighted magnetic resonance imaging. During the image analysis stage, the ligament region is automatically detected, localized, and extracted using fuzzy segmentation methods. Eight geometric features are defined for the ligament object. With a clinical reference database containing 107 cases of both healthy and pathological cases, a Fisher linear discriminant is used to select 4 most distinctive features. At the classification stage we employ five different soft computing classifiers to evaluate the feature vector suitability for the computerized ligament diagnosis. Among the classifiers we introduce and specify the particle swarm optimization based Sugeno-type fuzzy inference system and compare its performance to other established classification systems. The classification accuracy metrics: sensitivity, specificity, and Dice index all exceed 90% for each classifier under consideration, indicating high level of the proposed feature vector relevance in the computer aided ligaments diagnosis.

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Section: ])mentioning

confidence: 99%

Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis

Zarychta

Badura

Piętka

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…On the other hand, a too small max v may trap particles in a local optimum. PSO has been widely applied to various optimization problems and has obtained successful results [e.g., 22,[34][35][36]. For further discussions on PSO, its applications, and related resources, readers can refer to [29,37,38].…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Cost-based Procedure for Multi-response Parameter Design Problems Using GEP, Taguchi Quality Loss, and PSO: Case Study on Heat Sink Design

Hsu¹

2014

International Journal of Computational Intelligence Systems

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Most modern products/processes usually have several quality characteristics that must be optimized simultaneously; this is called a multi-response parameter design problem. To overcome shortcomings in the literature, including insufficient accuracy of second-order polynomials, subjective determination of relative weights and shape coefficients, and non-consideration of manufacturing or material costs, this paper proposes a cost-based procedure for resolving multi-response parameter design problems using gene expression programming (GEP), Taguchi quality loss, and particle swarm optimization (PSO). A case study with the aim of optimizing the design of a heat sink applied to a high-power MR16 LED lamp was used to demonstrate the proposed procedure. The experimental results indicated that the proposed procedure can provide highly robust settings for design parameters that can maximize the thermal performance and minimize the actual material cost of a heat sink. Furthermore, decisionmakers no longer need to subjectively determine the relative weight of each response. Therefore, the proposed approach can be considered to be feasible and effective; it has the potential to be a useful tool for resolving general multi-response parameter design problems in the real world.

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“…In this paper, PSO algorithm is employed to locate the region Y. PSO is a population-based heuristic search method, which is inspired by social behavior of bird flocking or fish schooling 20 . In the past several years, PSO was used to solve optimization problems in some fields successfully 21,22,23 . In this paper, the fitness function of PSO can be defined as:…”

Section: Correlation Analysismentioning

confidence: 99%

Content-based Image Hiding Method for Secure Network Biometric Verification

Che¹,

Kong²,

Gao³

et al. 2011

IJCIS

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For secure biometric verification, most existing methods embed biometric information directly into the cover image, but content correlation analysis between the biometric image and the cover image is often ignored. In this paper, we propose a novel biometric image hiding approach based on the content correlation analysis to protect the networkbased transmitted image. By using principal component analysis (PCA), the content correlation between the biometric image and the cover image is firstly analyzed. Then based on particle swarm optimization (PSO) algorithm, some regions of the cover image are selected to represent the biometric image, in which the cover image can carry partial content of the biometric image. As a result of the correlation analysis, the unrepresented part of the biometric image is embedded into the cover image by using the discrete wavelet transform (DWT). Combined with human visual system (HVS) model, this approach makes the hiding result perceptually invisible. The extensive experimental results demonstrate that the proposed hiding approach is robust against some common frequency and geometric attacks; it also provides an effective protection for the secure biometric verification.

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Applying particle swarm optimization algorithm for tuning a neuro-fuzzy inference system for sensor monitoring

Cited by 45 publications

References 8 publications

Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis

Comparative analysis of selected classifiers in posterior cruciate ligaments computer aided diagnosis

Cost-based Procedure for Multi-response Parameter Design Problems Using GEP, Taguchi Quality Loss, and PSO: Case Study on Heat Sink Design

Content-based Image Hiding Method for Secure Network Biometric Verification

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