1. Introduction to Neural Network and Neuro-DEVS Yan Wang August 29 th 2002

4. Example 1-Single Neuron Structure of a neuron in a neural net

5. Example 2-Three Layers Neural Net Neural net with three neuron layers

8. Perceptron Perceptron structure

9. Multi-Layer-Perceptron Multi-Layer-Perceptron structure

10. Backpropagation Net Backpropagation Net structure

11. Hopfield Net Hopfield Net structure

12. Kohonen Feature Map Kehonen Feature Map structure

18. Multi Layered Perceptron I/O are bounded in [0,1] for the activation to perform Pass 1: Forward Pass - Present inputs and let the activations flow until they reach the output layer. Pass 2: Backward Pass - Error estimates are computed for each output unit by comparing the actual output (Pass 1) with the target output. Then, these error estimates are used to adjust the weights in the hidden layer and the errors from the hidden layer are used to adjust the input layer.

19. Neuro-Atomic Model ANN designed by expert for specific purpose Trained ANNs stored in libraries ANN Object loaded while simulator is created

20. Neuro-Atomic Model (NAM) Description NAM=<X,Y,S,NN,ta,init,dint,dext, λ ,learn,act,prop> where: X = {R } is the set of input external event Y = {R } is the set of output external event S is the state set, where S = {(s,phase,error) s is the status {activated, learn, propagate} phase {active, passive} error {0,1} is the squared root error between the actual output and the desired output } NN is the link to the neural net object (ANN) ta: is the time advance function

22. Example - Solar Energetic System Solar panel, sun shinness and consumption are well known Battery shows better results with NN, use of NN as sub-component