Parallel Algorithm for Control Chart Pattern Recognition

Wani, M. Arif; Rashid, Shamima

doi:10.1109/icmla.2005.51

Cited by 10 publications

(15 citation statements)

References 8 publications

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“…Total accuracy [16] 93.94 [17] 95.19 [11] 96.28 Proposed features 97.83 system with respect to the inertia weight w and the two acceleration constants c 1 and c 2 , which control the behavior, and thus, the goodness of the PSO search process. In the first step, we fixed c 1 and c 2 to 1.6 and we varied w in the range [0, 1].…”

Section: Featuresmentioning

confidence: 99%

“…On the other hand, a featurebased approach is more flexible to deal with a complex process problem, especially when no prior information is available. If the features represent the characteristics of patterns explicitly and if their components are reproducible with the process conditions, the classifier recognition accuracy will increase [11]. Further, if the feature is amenable to reasoning, it will help in understanding how a particular decision was made, and this makes the recognition process a transparent process.…”

Section: Introductionmentioning

confidence: 97%

“…Further, if the feature is amenable to reasoning, it will help in understanding how a particular decision was made, and this makes the recognition process a transparent process. Features could be obtained in various forms, including principal component analysis shape features [9,11], correlation between the input and various reference vectors [16], and statistical correlation coefficients [17].…”

Section: Introductionmentioning

confidence: 99%

“…With this aim, several approaches have been proposed for CCP recognition. Some researchers have used expert systems [2], and others have used artificial neural networks (ANNs) [3][4][5][6][7][8][9][10][11][12][13]. ANNs can be simply classified into two main categories: supervised ANNs and unsupervised ANNs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Control chart pattern recognition using an optimized neural network and efficient features

Ebrahimzadeh

Ranaee

2010

ISA Transactions

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Section: Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Control chart pattern recognition using an optimized neural network and efficient features

Ebrahimzadeh

Ranaee

2010

ISA Transactions

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“…Among these 15 common types of CCPs, normal, trend, shift and cyclic patterns are the most basic and usually researched CCPs (Guh and Tannock 1999;Al-Assaf 2004;Assaleh and Al-assaf 2005). Many researchers classified further trend pattern into upward trend and downward trend, similarly for shift pattern and investigated the recognition methods for six types CCPs, namely normal (NOR), upward trend (UT), downward trend (DT), upward shift (US), downward shift (DS) and cyclic (CYC) (Pham and Oztemel 1994;Pham and Wani 1997;Hassan et al 2003;Wani and Rashid 2005;Wang and Kuo 2007;Cheng and Ma 2008;Pingyu et al 2009;Ebrahimzadeh and Ranaee 2010;Ebrahimzadeh et al 2012). Moreover, among numerous types control charts, x chart is one of the most important and useful on-line process monitoring and control tools.…”

Section: Introductionmentioning

confidence: 99%

Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

2015

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Accurate control chart patterns recognition (CCPR) plays an essential role in the implementation of control charts. However, it is a challenging problem since nonrandom control chart patterns (CCPs) are normally distorted by "common process variations". In this paper, a novel method of CCPR by integrating fuzzy support vector machine (SVM) with hybrid kernel function and genetic algorithm (GA) is proposed. Firstly, two shape features and two statistical features that do not depend on the distribution parameters and number of samples are presented to explicitly describe the characteristics of CCPs. Then, a novel multiclass method based on fuzzy SVM with a hybrid kernel function is proposed. In this method, the influence of outliers on classification accuracy of SVM-based classifiers is weakened by assigning a degree of membership for every training sample. Meanwhile, a hybrid kernel function combining Gaussian kernel and polynomial kernel is adopted to further enhance the generalization ability of the classifiers. To solve the issue of features selection and parameters optimization, GA is used to simultaneously optimize the input features subsets and parameters of fuzzy SVM-based classifier. Finally, several simulation experiments and a real example are addressed to validate the feasibility and effectiveness of the proposed methodology. And the results of simulation experiments demonstrate that it can achieve excellent performance for CCPR and outperforms other approaches, such as learning B Pingyu Jiang vector quantization network, multi-layer perceptron network, probability neural network, fuzzy clustering and SVM, in term of recognition accuracy. The results of the practical cases manifest that the proposed method has application potential for solving the problem of control chart interpretation in real-world.

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Recognition of control chart patterns using swarm intelligence and neural networks based on the statistical and shape features

Ebrahimzadeh

Ranaee

2010

IEEJ Transactions Elec Engng

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Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. In this paper, a hybrid intelligent system is proposed for the recognition of control chart patterns. In this system, we have used a proper set of shape features and statistical features as the efficient characteristics of the patterns. Then we proposed a hybrid heuristic recognition system based on particle swarm optimization to improve the generalization performance of the radial basis function neural network classifier. For this purpose, we have optimized the classifier design by searching for the best value of the parameters that tune its discriminate function. The obtained results show that the proposed technique has high recognition accuracy in comparison with other techniques. This recognition accuracy is achieved with fewer training samples.

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Parallel Algorithm for Control Chart Pattern Recognition

Cited by 10 publications

References 8 publications

Control chart pattern recognition using an optimized neural network and efficient features

Control chart pattern recognition using an optimized neural network and efficient features

Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

Recognition of control chart patterns using swarm intelligence and neural networks based on the statistical and shape features

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