Enhancing the Classification Efficiency of a Neural Model

Abstract

Neural networks are used to handle complex issues such as pattern identification (pattern classification), computer vision, voice and image recognition, and others. An intelligent gas sensor application is shown, which employs pattern categorization by neural networks with backpropagation and error correction (by comparing two errors). The published data from a thick film tin oxide sensor array is utilized to train a classifier. Its superior categorization and learning skills are shown by its capacity to discriminate between various forms of alcohol and alcoholic drinks. The novel model proposed in this paper improves classification accuracy.