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 Example of application in energy & engineering 2

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 3

Application fields

 Data analysis  Pattern recognition  Control application 4

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 5

Applications

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

Biological inspiration

  Brain structure Cell  Cell body  Axon  Denderites Dendrites Soma (cell body) Axon 7

Mathematical model

x 1 x 2 x 3 … x n-1 x n Node w 1 w 2 w 3 .

w n -1 w n

z



i n

  1

w i x i

;

y



H

(

z

)

Artificial neural cell

Output y 8

Mathematical model

Mathematic model of artificial neural cell Cell body

p w



b n f

 

wp



b



a

9

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 10

Activation function

Activation function

Linear Non-Linear Step Sigmoid Linear Gaussian 11

Learning

Supervised learning learning Unsupervised learning      Coincidence learning Performance learning Competitive learning Filter learning Spatiotemporal learning 12

Neuron types

    Hebb Perceptron Adaline Kohonen 13

Some issues

 Training dataset  Test dataset  Network size 14

   

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 15

References

2.

3.

81 زييا پ ،ريبكريما يتعنص هاگشناد تاراشتنا ، “ يبصع ياه هكبش ينابم ” ،رقاب دمحم جاهنم Hecht-Nielsen R., “ Neurocomputing “ , publishing company, 1991 .1

Addison-Wesley MATLAB help documentation 16