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NEURAL NETWORK

NEURAL NETWORK. By : Farideddin Behzad Supervisor : Dr. Saffar Avval May 2006 Amirkabir University of Technology. Agenda. Definition Application fields History Application Biological inspiration Mathematical model Basic definition Learning Neuron types and some issues

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NEURAL NETWORK

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  1. NEURAL NETWORK By : Farideddin Behzad Supervisor : Dr. Saffar Avval May 2006 Amirkabir University of Technology

  2. Agenda • Definition • Application fields • History • Application • Biological inspiration • Mathematical model • Basic definition • Learning • Neuron types and some issues • Example of application in energy & engineering

  3. Definition Haykin(1999) • massive parallel-distributed processor • natural propensity for storing experiential knowledge • available for use. • Acquiring knowledge by the network from its environment through a learning process • Using interneuron connection strengths, (a.k.a. synaptic weights), to store the acquired knowledge

  4. Application fields • Data analysis • Pattern recognition • Control application

  5. History • 1943, Warren McCulloch & Walter Pitts, works of neurons • 1960, Bernard Widrow & Marcian Hoff, developed ADALINE and MADLINE • From late 1960s to 1981, decreasing of researches • Early 1980s, renewed interest in neural network • 1986, Daivid Rummelhart & James McLand, error back-propagation algorithm

  6. Applications • Aerospace industry • Automotive industry • Banking • Military industry • Economics • Manufacturing • Medical applications • Oil & petroleum industry • And many more …

  7. Dendrites Soma (cell body) Axon Biological inspiration • Brain structure • Cell • Cell body • Axon • Denderites

  8. Mathematical model Node x1 x2 x3 … xn-1 xn w1 Output w2 Inputs y w3 . wn-1 wn Artificial neural cell

  9. Mathematical model Mathematic model of artificial neural cell Cell body input output

  10. Basic definition • Architecture: formal mathematical description of a Neural Network. (feed-forward & feed-back) • Layer or Slab: A subset of neurons in a neural network. (Input, Hidden, Output) • Episodical vs continuous networks • Neuron weight • Activation function

  11. Activation function Linear Activation function Non-Linear Sigmoid Linear Gaussian Step

  12. Learning Supervised learning • Coincidence learning • Performance learning • Competitive learning • Filter learning • Spatiotemporal learning learning Unsupervised learning

  13. Neuron types • Hebb • Perceptron • Adaline • Kohonen

  14. Some issues • Training dataset • Test dataset • Network size

  15. Example of application in energy • Soleimani. M, Thomas. B, Per Fahlen, “Estimation operative temperature of building using artificial neural network”, Journal of Energy and Building 38 ,2006 • Luis M. Romeo, Raquel Gareta, “ neural network for evaluating boiler behaviour”, Applied Thermal Engineering 26, 2006 • Seyedan B., Ching C.Y., “Sensitivity analysis of freestream turbulence parameter on stagnation region heat transfer using a neural network”, International Journal of Heat and Fluid Flow, 2006 • Perez-roa P., Vesovic V., “Air-pollution modelling in an urban area: Correlation turbulent diffusion coefficients by means of an artifical neral network approach”, Atmospheric Environment 40, 2006

  16. References • منهاج محمد باقر، ”مباني شبكه هاي عصبي“، انتشارات دانشگاه صنعتي اميركبير، پاييز81 • Hecht-Nielsen R., “Neurocomputing“, Addison-Wesley publishing company, 1991 • MATLAB help documentation

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