The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
Modeling brain function—the world of attractor neural networks
Modeling brain function—the world of attractor neural networks
Recursive neural networks for associative memory
Recursive neural networks for associative memory
Introduction to artificial neural systems
Introduction to artificial neural systems
Superlearning and neural network magic
Pattern Recognition Letters
A fuzzy recurrent artificial neural network (FRANN) for pattern classification
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Parallel Computer Architecture: A Hardware/Software Approach
Parallel Computer Architecture: A Hardware/Software Approach
Integrating database and World Wide Web technologies
World Wide Web
Better Web Searches and Prediction with Instantaneously Trained Neural Networks
IEEE Intelligent Systems
Information Retrieval on the World Wide Web
IEEE Internet Computing
Searching the Web: general and scientific information access
IEEE Communications Magazine
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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 all 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 on the Yang-Fen Automation Electrical Engineering Company as a case study. The experiment is continued for 4 months, and engineers are also used to operating the web-based pattern recognition system. Therefore, the cooperative plan described above is analyzed and discussed here. Finally, these papers propose the tradition methods compare with the innovative methods.