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 Neural Network Model for Time-Series Forecasting

The paper presents some aspects regarding the use of pattern recognition techniques and neural networks for the activity evolution diagnostication and prediction by means of a set of indicators. Starting from the indicators set there is defined a measure on the patterns set, measure representing a scalar value that characterizes the activity analyzed at each time moment. A pattern is defined by the values of the indicators set at a given time. Over the classes set obtained by means of the classification and recognition techniques is defined a relation that allows the representation of the evolution from negative evolution towards positive evolution. For the diagnostication and prediction the following tools are used: pattern recognition and multilayer perceptron. The paper also presents the REFORME software written by the authors and the results of the experiment obtained with this software for macroeconomic diagnostication and prediction during the years 2003-2010.time-series, pattern recognition, neural networks, multilayer perceptron, diagnostication, forecasting

Performance analysis of Handwritten Devnagari Character Recognition using Feed Forward , Radial Basis , Elman Back Propagation, and Pattern Recognition Neural Network Model Using Different Feature Extraction Methods

This paper describes the performance analysis for the four types of neural network with different feature extraction methods for character recognition of hand written devnagari alphabets. We have implemented four types of networks i.e. Feed forward , Radial basis, Elman back propagation and Pattern recognition neural network using three different types of feature extraction methods i.e. pixel value, histogram and blocks mean for each network. These algorithms have been performed better than the conventional approaches of neural network for pattern recognition. It has been analyzed that the Radial Basis neural network performs better compared to other types of networks

An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing

The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks

Experiences in Pattern Recognition for Machine Olfaction

Pattern recognition is essential for translating complex olfactory sensor responses into simple outputs that are relevant to users. Many approaches to pattern recognition have been applied in this field, including multivariate statistics (e.g. discriminant analysis), artificial neural networks (ANNs) and support vector machines (SVMs). Reviewing our experience of using these techniques with many different sensor systems reveals some useful insights. Most importantly, it is clear beyond any doubt that the quantity and selection of samples used to train and test a pattern recognition system are by far the most important factors in ensuring it performs as accurately and reliably as possible. Here we present evidence for this assertion and make suggestions for best practice based on these findings