Innovative Algorithms for Running a Web-Based Pattern Recognition Search System for a Component Patterns Database

Abstract

The real-time system uses a recurrent neural network (RNN) with associative memory for training and recognition. This study attempts to use associative memory to apply pattern recognition (PR) technology to the real-time pattern recognition of engineering components in a web-based recognition system with a Client-Server network structure. Remote engineers can draw the shape of the engineering components using the browser, and the recognition system then searches the component database via the Internet. Component patterns are stored in the database system considered here. Moreover, the data fields of each component pattern contain the properties and specifications of that pattern, except in the case of engineering components. The database system approach significantly improves recognition system capacity. The recognition system examined here employs parallel computing, which increases system recognition rate. The recognition system used in this work is an Internet-based, client-server network structure. The final phase of the system recognition applies database matching technology to processing recognition, and can solve the problem of spurious states. The system considered here is implemented in the Yang-Fen Automation Electrical Engineering Company as a case study. The experiment is continued for four months, and engineers are also used to operating the web-based pattern recognition system. Therefore, the cooperative plan described above is analysed and discussed here.