Improved Multi-Layer Perceptron for Recognition of Control Chart Pattern
نویسندگان
چکیده
منابع مشابه
A Bayesian Approach for the Recognition of Control Chart Patterns
In this research, an iterative approach is employed to recognize and classify control chart patterns. To do this, by taking new observations on the quality characteristic under consideration, the Maximum Likelihood Estimator of pattern parameters is first obtained and then the probability of each pattern is determined. Then using Bayes’ rule, probabilities are updated recursively. Finally, when...
متن کاملMulti-Layer Perceptron ensembles for increased performance and fault-tolerance in pattern recognition tasks
Multi-Layer Perceptrons (MLPs) have proven to be an e ective way to solve classi cation tasks. A major concern in their use is the di culty to de ne the proper network for a speci c application, due to the sensitivity to the initial conditions and to over tting and under tting problems which limit their generalization capability. Moreover, time and hardware constraints may seriously reduce the ...
متن کاملMulti-layer boosting for pattern recognition
We extend the standard boosting procedure to train a two-layer classifier dedicated to handwritten character recognition. The scheme we propose relies on a hidden layer which extracts feature vectors on a fixed number of points of interest, and an output layer which combines those feature vectors and the point of interest locations into a final classification decision. Our main contribution is ...
متن کاملAn improved scheme for online recognition of control chart patterns
This paper proposes two alternative schemes for the online recognition of control chart patterns (CCPs), namely: 1 a scheme based on direct continuous recognition 2 a scheme based on ‘recognition only when necessary’. The study focuses on recognition of six CCPs plotted on the Shewhart X-bar chart, namely, random, shift-up, shift down, trend-up, trend-down and cyclic. The artificial neural netw...
متن کاملAN IMPROVED CONTROLLED CHAOTIC NEURAL NETWORK FOR PATTERN RECOGNITION
A sigmoid function is necessary for creation a chaotic neural network (CNN). In this paper, a new function for CNN is proposed that it can increase the speed of convergence. In the proposed method, we use a novel signal for controlling chaos. Both the theory analysis and computer simulation results show that the performance of CNN can be improved remarkably by using our method. By means of this...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2020
ISSN: 0975-8887
DOI: 10.5120/ijca2020920537