In this paper, a palmprint recognition system using bit-plane extraction with MLP neural network is presented. It is an approach where the palmprint feature is extracted by slicing the image into 8 bit-planes. The extracted bit-planes are then serves as input data into the neural network. MLP neural network is applied to train and test images for recognition. Networks are simulated for a few co...

Manufacturing processes have become highly accurate and precise in recent years, particularly the chemical, aerospace, electronics industries. This has attracted researchers to investigate improved procedures for monitoring detection of small process variations remain line with such advances. Among these techniques, statistical controls (SPC), particular control chart pattern (CCP), a popular c...

The best pattern recognizers in most instances are human, yet we do not understand how human recognize patterns. The pattern recognition is critical in the human decision task, the more relevant the pattern at your disposal, the better your decision will be. More recently, neural network techniques in pattern recognition have been receiving increasing attention. The design of a recognition syst...

A new neural network architecture is introduced which may be used for fault-tolerant general pattern recognition. Images are learned by extracting features at each layer. These same images may later be recognized by extracting features which are then used to constrain a search for additional features to validate one of a set of chosen image representation candidates. Unsupervised learning of fe...

This work presents a neural network model for the clustering analysis of data based on Self Organizing Maps (SOM). The model evolves during the training stage towards a hierarchical structure according to the input requirements. The hierarchical structure symbolizes a specialization tool that provides refinements of the classification process. The structure behaves like a single map with differ...

This paper proposes an object recognition system that is invariant to rotation, translation and scale and can be trained under partial supervision. The system is divided into two sections namely, feature extraction and recognition sections. Feature extraction section uses proposed rotation, translation and scale invariant features. Recognition section consists of a novel Reflex Fuzzy MinMax Neu...

Pattern recognition in control charts is critical to make a balance between discovering faults as early as possible and reducing the number of false alarms. This work is devoted to designing a multistage neural network ensemble that achieves this balance which reduces rework and scrape without reducing productivity. The ensemble under focus is composed of a series of neural network stages and a...