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Ali Setoodehnia; Ph.D. Kean University Union, NJ 07083 asetoodekean 908-737-3507

2. AGENDA . INTRODUCTION ARTIFCIAL NEURAL NETWORKVIRTUAL COMPUTER LABORATORYSIMULATIONSAPPLICATIONSSUMMARY. 3. ARTIFICIAL NEURAL NETWORK (ANN). What is ANN?ANN is parallel processing technique which is a new form of solution for nonlinear systems.ANN is MLIN with SLR for updating the conne

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Ali Setoodehnia; Ph.D. Kean University Union, NJ 07083 asetoodekean 908-737-3507

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    1. 1 Ali Setoodehnia; Ph.D. Kean University Union, NJ 07083 asetoode@kean.edu 908-737-3507

    2. 2 AGENDA INTRODUCTION ARTIFCIAL NEURAL NETWORK VIRTUAL COMPUTER LABORATORY SIMULATIONS APPLICATIONS SUMMARY

    3. 3 ARTIFICIAL NEURAL NETWORK (ANN) What is ANN? ANN is parallel processing technique which is a new form of solution for nonlinear systems. ANN is MLIN with SLR for updating the connection weights. Why ANN? Conventional technique are very successful in some area like linear system, but for nonlinear systems there is question.

    4. 4 Learning Process A typical ANN learning process is based on the following characteristics Learning mechanism Learning modes Learning rate Learning law Architecture

    5. 5 Feed-Forward Network (FFN) What is FFMN? A FFN is a net with one or more layers of neurons between inputs and the output units , which the signals flow from the input units to the output units, in a forward direction.

    6. 6 FFN ARCHITECTURE

    7. 7 XOR - MODELS Single Neuron

    8. 8 XOR-MODELS Two Layer

    9. 9 XOR-MODELS Three Layer

    10. 10 LEARNING RULE Hebb Perceptron ADALIN BackPropagation

    11. 11 HEBB RULE If both X(input) and Y(output) are active (ON) then W(t+1) = W(t) + X*Y This is good for logic functions and character recognition Data representation can be Binary : 0,1 Bipolar: +1, 0, -1

    12. 12 PERCEPTRON RULE If error (input - output) is not zero then W(t+1) = W(t) + * X*Y where learning rate

    13. 13 ADELIN Rule If the error in not less than threshold then W(t+1) = W(t) + * (target - output_ measur.)

    14. 14 BACKPROPAGATION path If error is not less than threshold then for the hidden layers the weights are updated as: ?Wij(t) = *(Zj)(Sk((Zk)*ek)Yi + *?Wij(t-1) where is momentum factor, and f is tanh() function or sigmoid function. And for output layer ?Wjk(t) = *(Zk)(dk - Yk)*Yj + *?Wjk(t-1)

    15. 15 MODIFIED FFMN Installing ARMA filter at each neuron CONVERGENCE

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    17. 17 VIRTUAL COMPUTER LABORATORY (VCL) What is VCL? This VCL is a dynamic internet sit for ANN users. What is dynamic internet? Not Static. Server will run programs to generate information. Computations are not available in advance on the Client-Server.

    18. 18 Client-Server CLIENT: APPLET

    19. 19 JAVA/APPLET Over Internet What is Applet? APPLET is an important feature of Java programming language that they can be easily accessible over the Internet using WWW browser such as Internet Explorer or others. How does Applet work? Write the Applet on the server side at the LOCATION: IPaddress\WWW\AppletFilename Link the AppletFilename.class with index.html page

    20. 20 Example: NameOfApplet.htm <HTML> <HEAD> <title> welcome Java Applet </title> </HEAD> <BODY> <P> </P> <!-- Insert HTML here --> <applet code=AppletProgramName.class width=320 height=200 </applet> </BODY> </HTML>

    21. 21 VCL Simulation The following procedure is used for VCL and running experiment: Step-1: Get access to the Internet browser Step-2: Type VCLs URL address Step-3: Choose an option Step-4: Enter the data in the Text Fields Step-5: Click Run Step-6: see the plot for Convergence check! Step-7: Save the model! EXAMPLE

    22. 22 SIMULATION XOR problem other nonlinear problems Grids Short term forecasting M_C nonlinear function Henon function etc

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    31. 31 APPLICATIONS Pattern recognition Image processing Prediction Robotics Control system etc.

    32. 32 SUMMARY ANN technology represent a paradigm shift in real world problem solving techniques. With this technology, computers are now able to tackle problems whose underlying structure is not understood and now need not be understood.

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