338,020 research outputs found
Comparing Statistical Feature and Artificial Neural Networks for Control Chart Pattern Recognition: A Case Study
Control chart has been widely used for monitoring production process, especially in
evaluating the quality performance of a product. An uncontrolled process is usually known by
recognizing its chart pattern, and then performing some actions to overcome the problems. In high
speed production process, real-time data is recorded and plotted almost automatically, and the control
chart pattern needs to be recognized immediately for detecting any unusual process behavior. Neural
networks for automatic control chart recognition have been studied in detecting its pattern. In the field
of computer science, the performance of its automatic and fast recognition ability can be a substitution
for a conventional method by human. Some researchers even have developed newer algorithm to
increase the recognition process of this neural networks control chart. However, artificial approaches
have some difficulties in implementation, especially due to its sophisticated programming algorithm.
Another competing method, based on statistical feature also has been considered in recognition
process. Control chart is related to applied statistical method, so it is not unreasonable if statistical
properties are developed for its pattern recognition. Correlation coefficient, one of classic statistical
features, can be applied in control chart recognition. It is a simpler approach than the artificial one. In
this paper, the comparison between these two methods starts by evaluating the behavior of control
chart time series point, and measured for its closeness to some training data that are generated by
simulation and followed some unusual control chart pattern. For both methods, the performance is evaluated by comparing their ability in detecting the pattern of generated control chart points. As a sophisticated method, neural networks give better recognition ability. The statistical features method simply calculate the correlation coefficient, even with small differences in recognizing the generated pattern compared to neural networks, but provides easy interpretation to justify the unusual control chart pattern. Both methods are then applied in a case study and performances are then measured
A New Method for Fast Computation of Moments Based on 8-neighbor Chain CodeApplied to 2-D Objects Recognition
2D moment invariants have been successfully applied in pattern recognition tasks. The main difficulty of using moment invariants is the computational burden. To improve the algorithm of moments computation through an iterative method, an approach for fast computation of moments based on the 8-neighbor chain code is proposed in this paper. Then artificial neural networks are applied for 2D shape recognition with moment invariants. Compared with the method of polygonal approximation, this approach shows higher accuracy in shape representation and faster recognition speed in experiment
A novel approach to error function minimization for feedforward neural networks
Feedforward neural networks with error backpropagation (FFBP) are widely
applied to pattern recognition. One general problem encountered with this type
of neural networks is the uncertainty, whether the minimization procedure has
converged to a global minimum of the cost function. To overcome this problem a
novel approach to minimize the error function is presented. It allows to
monitor the approach to the global minimum and as an outcome several
ambiguities related to the choice of free parameters of the minimization
procedure are removed.Comment: 11 pages, latex, 3 figures appended as uuencoded fil
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