Skip to main content

Part of the book series: Advanced Topics in Science and Technology in China ((ATSTC))

Abstract

As an introduction to this book, we will review the development history of artificial intelligence and neural networks, and then give a brief introduction to and analysis of some important problems in the fields of current artificial intelligence and intelligent information processing. This book will begin with the broad topic of “artificial intelligence”, next examine “computational intelligence”, then gradually turn to “neural computing”, namely, “artificial neural networks”, and finally explain “process neural networks”, of which the theories and applications will be discussed in detail.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. McCulloch W.S., Pitts W.H. (1943) A logical calculus of the ideas imminent in euron activity. Bulletin Mathematical Biophysics 5(1):115–133

    Article  MATH  MathSciNet  Google Scholar 

  2. Hebb D.O. (1949) The Organization of Behavior: A Neuropsychological Theory. Wiley, New York

    Google Scholar 

  3. Rosenblatt F. (1958) Principles of Neuro-Dynamics. Spartan Books, New York

    Google Scholar 

  4. Widrow B. (1962) Generalization and Information Storage in Networks of Adaline Neurons. In: Self-Organizing Systems. Spartan, Washington DC, pp.435–461

    Google Scholar 

  5. Amari S.A. (1967) Theory of adaptive pattern classifiers. IEEE Transaction Electronic Computers 16(3):299–307

    Article  MATH  MathSciNet  Google Scholar 

  6. Minsky M.L., Papert S.A. (1969) Perceptrons. MIT Press, Cambridge MA

    MATH  Google Scholar 

  7. Amari S. (1972) Characteristics of random nets of analog neuron-like elements. IEEE Transaction on Systems, Man, Cybernetics 5(2):643–657

    Google Scholar 

  8. Anderson J.A. (1972) A simple neural network generating interactive memory. Mathematical Biosciences 14:197–220

    Article  MATH  Google Scholar 

  9. Grossberg S. (1976) Adaptive pattern classification and universal recoding. I: Parallel development and coding of neural feature detectors. Biological Cybernetics 23(3):121–134

    Article  MATH  MathSciNet  Google Scholar 

  10. Hopfield J.J. (1982) Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science, U.S.A. 79(2):554–558

    MathSciNet  Google Scholar 

  11. Rumelhart D.E., Hinton G.E., Williams R.J. (1986) Learning representations of ack-propagating errors. Nature 323(9):533–536

    Article  Google Scholar 

  12. Hinton G.E., Nowlan S.J. (1987) How learning can guide evolution. Complex systems 1(3):495–502

    MATH  Google Scholar 

  13. Hecht-Nielsen R. (1989) Theory of the back-propagation neural network. Proceedings of the International Joint Conference on Neural Networks 1:593–605

    Article  Google Scholar 

  14. Funahashi, K. (1989) On the approximate realization of continuous mappings by neural networks. Neural Networks 2(3):183–192

    Article  Google Scholar 

  15. Hornik K., Stinchcombe M., White H. (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feedforword networks. Neural Networks 3(5):551–560

    Article  Google Scholar 

  16. Linsker R. (1988) Towards an organizing principle for a layered perceptual network. Neural Information Processing Systems 21(3):485–494

    Google Scholar 

  17. Boser B.E., Guyon I.M., Vapnik V.N. (1992) A training algorithm for optimal margin classifiers. In: Haussler D., Ed. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory. ACM Press, Pittsburgh, PA, pp.144–152.

    Chapter  Google Scholar 

  18. Vapnik V.N. (1995) The Nature of Statistical Learning Theory. Springer, New York

    MATH  Google Scholar 

  19. Vapnik V.N. (1998) Statistical Learning Theory. Wiley, New York

    MATH  Google Scholar 

  20. Han M., Wang Y. (2009) Analysis and modeling of multivariate chaotic time series based on neural network. Expert Systems with Applications 36(2):1280–1290

    Article  Google Scholar 

  21. Abdelhakim H., Mohamed E.H.B., Demba D., et al. (2008) Modeling, analysis, and neural network control of an EV electrical differential. IEEE Transactions on Industrial Electronics 55(6):2286–2294

    Article  Google Scholar 

  22. Al Seyab R.K., Cao Y. (2008) Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation. Journal of Process Control 18:568–581

    Article  Google Scholar 

  23. Tomohisa H., Wassim M.H., Naira H., et al. (2005) Neural network adaptive control for nonlinear nonnegative dynamical systems. IEEE Transactions on Neural Networks 16(2):399–413

    Article  Google Scholar 

  24. Tomohisa H., Wassim M.H., Naira H. (2005) Neural network adaptive control for nonlinear uncertain dynamical systems with asymptotic stability guarantees. In: 2005 American Control Conference pp. 1301–1306

    Google Scholar 

  25. Ghiassi M., Saidane H., Zimbra D.K. (2005) A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting 21(2):341–362

    Article  Google Scholar 

  26. Tan Y.H., He Y.G., Cui C, Qiu G.Y. (2008) A novel method for analog fault diagnosis based on neural networks and genetic algorithms. IEEE Transactions on Instrumentation and Measurement 57(11):1221–1227

    Google Scholar 

  27. He X.G., Liang J.Z. (2000) Process neural networks. In: World Computer Congress 2000, Proceedings of Conference on Intelligent Information Processing. Tsinghua University Press, Beijing, pp. 143–146

    Google Scholar 

  28. He X.G., Liang J.Z. (2000) Some theoretical issues on procedure neural networks. Engineering Science 2(12):40–44 (in Chinese)

    Google Scholar 

  29. Zadeh L.A. (1965) Fuzzy sets. Information and Control 8:338–353

    Article  MATH  MathSciNet  Google Scholar 

  30. He X.G. (1989) Weighted fuzzy logic and wide application. Chinese Journal of Computer 12(6):458–464 (in Chinese)

    Google Scholar 

  31. He X.G. (1990) Fuzzy computational reasoning and neural networks. Proceedings of the Second International Conference on Tools for Artificial Intelligence. Herndon, VA, pp.706–711

    Google Scholar 

  32. Holland J. (1975) Adaptation in Natural and Artificial Systems. Univ. of Michigan Press, Ann Arbor

    Google Scholar 

  33. Malheiros-Silveira G.N., Rodriguez-Esquerre V.F. (2007) Photonic crystal band gap optimization by generic algorithms. Microwave and Optoelectronics Conference, SBMO/IEEE MTT-S International pp.734–737

    Google Scholar 

  34. Feng X.Y., Jia J.B., Li Z. (2000) The research of fuzzy predicting and its application in train’s automatic control. Proceedings of the 13th International Conference on Pattern Recognition pp.82–86

    Google Scholar 

  35. Gofman Y., Kiryati N. (1996) Detecting symmetry in grey level images: the global optimization approach. Proceedings of 2000 International Workshop on Autonomous Decentralized Systems 1:889–894

    Google Scholar 

  36. Fogarty T.C. (1989) The machine learning of rules for combustion control in multiple burner installations. Proceedings of Fifth Conference on Artificial Intelligence Applications pp.215–221

    Google Scholar 

  37. Matuki T., Kudo T., Kondo T. (2007) Three dimensional medical images of the lungs and brain recognized by artificial neural networks. SICE, Annual Conference pp.1117–1121

    Google Scholar 

  38. Fogel L.J., Owens A.J., Walsh M.J. (1966) Artificial Intelligence Through Simulated Evolution. Wiley, New York

    MATH  Google Scholar 

  39. Swain A.K., Morris A.S. (2000) A novel hybrid evolutionary programming method for function optimization. Proceedings of the 2000 Congress on Evolutionary Computation 1:699–705

    Google Scholar 

  40. Dehghan M., Faez K., Ahmadi M. (2000) A hybrid handwritten word recognition using self-organizing feature map, discrete HMM, and evolutionary programming. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks 5:515–520

    Google Scholar 

  41. Li X.L., He X.D., Yuan S.M. (2005) Learning Bayesian networks structures from incomplete data based on extending evolutionary programming. Proceedings of 2005 International Conference on Machine Learning and Cybernetics 4:2039–2043

    Google Scholar 

  42. Lieslehto J. (2001) PID controller tuning using evolutionary programming. Proceedings of the 2001 American Control Conference 4:2828–2833

    Google Scholar 

  43. Li Y. (2006) Secondary pendulum control system based on genetic algorithm and neural network. IEEE Control Conference pp. 1152–1155

    Google Scholar 

  44. Jose J.T., Reyes-Rico C, Ramirez J. (2006) Automatic behavior generation in a multi-agent system through evolutionary programming. Robotics Symposium, IEEE 3rd Latin American pp.2–9

    Google Scholar 

  45. Gao W. (2004) Comparison study of genetic algorithm and evolutionary programming. Proceedings of 2004 International Conference on Machine Learning and Cybernetics 1:204–209

    Google Scholar 

  46. Back T., Hoffmeister F., Schefel H.P. (1991) A survey of evolution strategies. Proceedings of the Fourth ICGA. Morgan Kaufmann Publishers, Los Altos, CA, pp.2–9

    Google Scholar 

  47. He X.G. (1996) Fuzzy reasoning network and calculation inference. Journal of Software (10):282–287 (in Chinese)

    Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Zhejiang University Press, Hangzhou and Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

(2009). Introduction. In: Process Neural Networks. Advanced Topics in Science and Technology in China. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73762-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73762-9_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73761-2

  • Online ISBN: 978-3-540-73762-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics