An overview of neural network applications

Neural networks attempt to use human-like computing strategies to improve the performance of computers for artiicial intelligence applications. A short introduction about neural networks and an overview of application areas is given.

[1]  A. P. de Weijer,et al.  Modelling of Industrial Processes using Natural Computation , 1995, SNN Symposium on Neural Networks.

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

[3]  Paul J. Werbos,et al.  Neural Networks for Control: An Overview , 1990, 1990 American Control Conference.

[4]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[5]  David E. Rumelhart,et al.  Predicting the Future: a Connectionist Approach , 1990, Int. J. Neural Syst..

[6]  S. Amari.  A Theory ofAdaptive Pattern Classifiers , 1967 .

[7]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[8]  Heskes,et al.  Learning in neural networks with local minima. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[9]  J. C. Wiist.  Current Prediction for Shipping Guidance , 1995 .

[10]  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.

[11]  Simon Haykin,et al.  Neural networks , 1994 .

[12]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[13]  Hilbert J. Kappen.  Deterministic learning rules for boltzmann machines , 1995, Neural Networks.