1. Soft Computing Lecture 8 Using of perceptron for image recognition and forecasting

2. –Stopping criterion The algorithm terminates when the change in the criterion function J(w) is smaller than some preset value  There are other stopping criteria that lead to better performance than this one So far, we have considered the error on a single pattern, but we want to consider an error defined over the entirety of patterns in the training set The total training error is the sum over the errors of n individual patterns

3. –Stopping criterion (cont.) A weight update may reduce the error on the single pattern being presented but can increase the error on the full training set However, given a large number of such individual updates, the total error of equation (1) decreases

5. How much neurons in hidden layers are needed For solving of task regression with 1 hidden layer it is needed more then 2N y, where N y – number of output neurons Number of weights in MLP N w where N p – number of patterns during training, N x, N y – numbers of input and output neurons, correspondingly

6. How much neurons in hidden layers are needed (cont.) Number of hidden neurons for MLP with 1 hidden layer:

7. Preparing of training examples It is needed to select features sufficient for classification If needed features are absent in primary features and ones are easy calculated then it is needed to calculate sufficient features from primary ones Often scaling is needed –Different ranges of input vector must be reduced to similar ranges It is needed to mix examples of different classes in training sequence It is needed to select most representative examples of each classes It is needed to remove from sequence equal examples

8. Using of perceptrons for forecasting of time series Let the example for training includes 7 points as inputs and 8 th as output of neural network. For 1th example these are points 1-8. Every next example is previous shifted by 1 point to right. After training we can to forecast value in point 30.

9. For possibility of forecasting by neural networks it is needed 1)to subtract trend from values of electricity to obtain any random periodic series. 2) or to learn to forecast changing of producing, 3) If it is reason to think that the form of function is strongly changed it is needed to select a period for training. Example of necessity to preliminary processing of data

10. Illustration of patterns in bar chart form in any forecasting system of stock

11. Illustration of a trading chart

12. Example of forecasting of time series

13. Problems of using of MLP It is needed to prepare data for training It is needed to select parameters of neural network and learning algorithm for training Program shells for building and using of neural networks aims to automatize solving of these problems

14. Examples of neural simulator NeuroSolutions –www.nd.com Brain Maker –www.calsci.com In software Statistics –www.sas.com Neural Works Predict/Professional –www.neuralware.com Forecaster XL –www.alyuda.com Neural Bench –www.neuralbench.ru