Artificial neural network: Framework for fault tolerance and future

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, artificial neural network techniques in pattern recognition have been receiving increasing concentration and awareness. It addressed the question of whether neural networks are inherently fault tolerant. Neural networks were visualized from an abstract functional level rather than a physical implementation level to allow their computational fault tolerance to be assessed and to be understood. The design of a recognition system requires concentrating on the following aspects: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, learning, selection of training and test samples.