Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first c...

Two neural classifiers were developed for image recognition: PCNC (Permutation Coding Neural Classifier) and LIRA (Limited Receptive Area) neural classifiers. These neural classifiers are multipurpose neural classifiers. We applied them in micromechanics. Information about shape and texture of the micro workpiece can be used to improve precision of both assembly and manufacturing processes. The...

This paper proposes a novel and simple local neural classifier for the recognition of mental tasks from on-line spontaneous EEG signals. The proposed neural classifier recognizes three mental tasks from on-line spontaneous EEG signals. Correct recognition is around 70%. This modest rate is largely compensated by two properties, namely low percentage of wrong decisions (below 5%) and rapid respo...

The success of an ensemble of classifiers depends on the diversity of the underlying features. If a classifier can address more different aspects of the analyzed objects, this allows to improve an ensemble. In this paper we propose an ensemble using as classifier members a Hopfield Neural Network (HNN) that uses Haar-like features as an input template. We analyse the HNN as the only classifier ...

Human face communicates important information about a person’s emotional condition. In this paper an approach for facial expression recognition using wavelet transform for feature extraction and neural network classifier for five basic emotions is proposed. The strength of the algorithm is the reduction in feature size and use of less number of images for training the network, compared to exist...

Two neural classifiers were developed for image recognition: PCNC (Permutation Coding Neural Classifier) and LIRA (Limited Receptive Area) neural classifiers. These neural classifiers are multipurpose neural classifiers. We applied them in micromechanics. Information about shape and texture of the micro workpiece can be used to improve precision of both assembly and manufacturing processes. The...

Abstract. Neural networks are growing in popularity today as a tool for classification of remotely sensed images. Majority of the neural network applications involve the error backpropagation algorithm for training the network to work as a classifier. While this algorithm has been successfully employed in various image classification problems, the errors associated with per-pixel classifier sti...

Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), multiple classifier combination, etc. In this paper, we discuss the characteristics of the ...