Web-based search system of pattern recognition for the pattern of industrial component by an innovative technology

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.

[1]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[2]  S Mangrulkar,et al.  Artificial neural systems. , 1990, ISA transactions.

[3]  C. Lee Giles,et al.  Searching the Web: general and scientific information access , 1999, First IEEE/POPOV Workshop on Internet Technologies and Services. Proceedings (Cat. No.99EX391).

[4]  Jinwen Ma.  A Neural Network Approach to Real-Time Pattern Recognition , 2001, Int. J. Pattern Recognit. Artif. Intell..

[5]  E. Munoko,et al.  Computers in Industry , 1963, Nature.

[6]  Martin Hasler,et al.  Recursive neural networks for associative memory , 1990, Wiley-interscience series in systems and optimization.

[7]  W. A. Little,et al.  Analytic study of the memory storage capacity of a neural network , 1978 .

[8]  Robert P. W. Duin,et al.  Superlearning and neural network magic , 1994, Pattern Recognit. Lett..

[9]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[10]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[11]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[12]  Daniel J. Amit,et al.  Modeling brain function: the world of attractor neural networks, 1st Edition , 1989 .

[13]  Hugh Garraway.  Parallel Computer Architecture: A Hardware/Software Approach , 1999, IEEE Concurrency.

[14]  Santosh S. Venkatesh,et al.  The capacity of the Hopfield associative memory , 1987, IEEE Trans. Inf. Theory.

[15]  Anoop Gupta,et al.  Parallel computer architecture - a hardware / software approach , 1998 .

[16]  Hongjun Lu,et al.  Integrating database and World Wide Web technologies , 2004, World Wide Web.

[17]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[18]  Thomas B. Kepler,et al.  Optimal learning in neural network memories , 1989 .

[19]  Jinwen Ma.  The Stability of the Generalized Hopfield Networks in Randomly Asynchronous Mode , 1997, Neural Networks.

[20]  Demetri Psaltis,et al.  Linear and logarithmic capacities in associative neural networks , 1989, IEEE Trans. Inf. Theory.

[21]  Sameer Singh,et al.  A Long Memory Pattern Modelling and Recognition System for Financial Time-Series Forecasting , 1999, Pattern Analysis & Applications.

[22]  Roelof K. Brouwer.  An integer recurrent artificial neural network for classifying feature vectors , 1999, ESANN.

[23]  M. Perus,et al.  NEURAL NETWORKS AS A BASIS FOR QUANTUM ASSOCIATIVE NETWORKS MITJA PERUŠ Institute BION , Stegne 21 , SLO-1000 Ljubljana , Slovenia mitja , 2004 .

[24]  Robert P. W. Duin,et al.  The effective capacity of multilayer feedforward network classifiers , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[25]  Giles,et al.  Searching the world wide Web , 1998, Science.

[26]  Roelof K. Brouwer.  A Fuzzy Recurrent Artificial Neural Network (FRANN) for Pattern Classification , 2000, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[27]  Vijay V. Raghavan,et al.  Information Retrieval on the World Wide Web , 1997, IEEE Internet Comput..

[28]  Subhash Kak.  Better Web Searches and Prediction with Instantaneously Trained Neural Networks , 1999 .

[29]  Z. Tan,et al.  PATTERN RECOGNITION WITH STOCHASTIC RESONANCE IN A GENERIC NEURAL NETWORK , 2000 .

[30]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .