Web-based search system of pattern recognition for the pattern of industrial components by an innovate technology

This work utilizes the pattern recognition (PR) technologies by Client-Server network structure into a web-based recognizing system. The system uses a recurrent neural network (RNN) with associative memory to perform the action of training and recognition. Client-end engineer is able to draw directly the shape of engineering components by the browser, and the recognition system will proceed with search for the component database of company by the structure of Internet. In this paper, these component patterns are stored in the database system. Their properties and specifications are also attached to the data field of each component pattern except the pattern of engineering component. These Component patterns with the approach of database system will be able to improve the capacity of recognition system effectively. In our approach, the recognition system adopts parallel computing, and it will raise the recognition rate of system. Our recognition system is a client-server network structure by Internet. The last phase joins the technology of database matching in process of the recognition, and it will solve the problem of spurious state. In this paper, our study will be carried out in the Yang-Fen Automation Electrical Engineering Company. Therefore, the cooperative plan of above context will be analyzed and discussed in this paper.

[1]  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 .

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

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

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

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

[6]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[7]  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).

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

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

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

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

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

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

[14]  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).

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

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

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

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

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

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

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

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

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

[24]  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.

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