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

2015 ◽  
Vol 29 (1) ◽  
pp. 51-67 ◽  
Author(s):  
Xueliang Zhou ◽  
Pingyu Jiang ◽  
Xianxiang Wang
Keyword(s):  
Control Chart ◽  
Kernel Function ◽  
Control Chart Patterns ◽  
Chart Patterns ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

A Conceptual Methodology for Recognition of Constrained Control Chart Patterns

2013 ◽  
Vol 845 ◽  
pp. 696-700
Author(s):  
Razieh Haghighati ◽  
Adnan Hassan
Keyword(s):  
Pattern Recognition ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Fault Tolerant ◽  
Constrained Control ◽  
Statistical Process ◽  
Control Chart Patterns ◽  
Control Chart Pattern ◽  
Chart Patterns

Traditional statistical process control (SPC) charting techniques were developed to monitor process status and helping identify assignable causes. Unnatural patterns in the process are recognized by means of control chart pattern recognition (CCPR) techniques. There are a broad set of studies in CCPR domain, however, given the growing doubts concerning the performance of control charts in presence of constrained data, this area has been overlooked in the literature. This paper, reports a preliminary work to develop a scheme for fault tolerant CCPR that is capable of (i) detecting of constrained data that is sampled in a misaligned uneven fashion and/or be partly lost or unavailable and (ii) accommodating the system in order to improve the reliability of recognition.

Fuzzy complementary entropy using hybrid-kernel function and its unsupervised attribute reduction

2021 ◽  
Vol 231 ◽  
pp. 107398
Author(s):  
Zhong Yuan ◽  
Hongmei Chen ◽  
Xiaoling Yang ◽  
Tianrui Li ◽  
Keyu Liu
Keyword(s):  
Kernel Function ◽  
Attribute Reduction ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

Control chart pattern clustering using a new self-organizing spiking neural network

2008 ◽  
Vol 222 (10) ◽  
pp. 1201-1211 ◽  
Author(s):  
D T Pham ◽  
M S Packianather ◽  
E Y A Charles
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Neural Network Model ◽  
Spiking Neurons ◽  
Spiking Neural Network ◽  
Pattern Clustering ◽  
Control Chart Patterns ◽  
Chart Patterns ◽  
Spiking Neural Network Model ◽  
Self Organizing

This paper focuses on the architecture and learning algorithm associated with using a new self-organizing delay adaptation spiking neural network model for clustering control chart patterns. This temporal coding spiking neural network model employs a Hebbian-based rule to shift the connection delays instead of the previous approaches of delay selection. Here the tuned delays compensate the differences in the input firing times of temporal patterns and enables them to coincide. The coincidence detection capability of the spiking neuron has been utilized for pattern clustering. The structure of the network is similar to that of a Kohonen self-organizing map (SOM) except that the output layer neurons are coincidence detecting spiking neurons. An input pattern is represented by the neuron that is the first to fire among all the competing spiking neurons. Clusters within the input data are identified with the location of the winning neurons and their firing times. The proposed self-organized delay adaptation spiking neural network (SODA_SNN) has been utilized to cluster control chart patterns. The trained network obtained an average clustering accuracy of 96.1 per cent on previously unseen test data. This was achieved with a network of 8 × 8 spiking neurons trained for 20 epochs containing 1000 training examples. The improvement in clustering accuracy achieved by the proposed SODA_SNN on the unseen test data was twice as much as that on the training data when compared to the SOM.

Recognition of control chart patterns using an intelligent technique

2013 ◽  
Vol 13 (5) ◽  
pp. 2970-2980 ◽  
Author(s):  
Ata Ebrahimzadeh ◽  
Jalil Addeh ◽  
Vahid Ranaee
Keyword(s):  
Control Chart ◽  
Intelligent Technique ◽  
Control Chart Patterns ◽  
Chart Patterns
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