By Vassil Sgurev, Ronald R. Yager, Janusz Kacprzyk, Vladimir Jotsov
This publication offers a huge number of various modern IT equipment and functions in clever structures is displayed. each ebook bankruptcy represents an in depth, particular, a long way attaining and unique re-search in a respective medical and functional box. although, all the chapters proportion the typical element of robust similarity in a feeling of being leading edge, appropriate and collectively appropriate with one another. In different phrases, the equipment from the various chapters will be considered as bricks for construction the subsequent new release “thinking machines” in addition to for different futuristic logical purposes which are speedily altering our international nowadays.
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Extra resources for Innovative Issues in Intelligent Systems
Example text
It is observed that the bigger the disarray with data the higher the hope of exotic unknown technologies to clean up the mess. Usually this behavior is initiated by top management who are unaware of the nasty reality of the mess. 0 effect. The best winning strategy is to define the requirements and the expectations in advance and to communicate clearly to the user the limitations of the methods. Better to reject an impossible implementation than to poison the soil for many feasible intelligent systems applications in the future.
Shafi et al. 1 Performance Measures We applied the commonly used performance measures in forecasting problems to compare and evaluate the accuracy of our model. These performance measures are Root Mean Square Error (RMSE), Mean Average Error (MAE) and Coefficient of Determination (R2) between the desired learner contexts and the learning content. Which are calculated by 1 X yq À ~yq ; N q¼1 Q MAE ¼ ð12Þ RMSE is defined as the square root of the mean squared error as: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u Q u1 X RMSE ¼ t ðyq À ~yq Þ2 N q¼1 ð13Þ The difference of the RMSE between observed and predicted values was computed for each trial with different epoch numbers, and the best structure was determined by the lowest value of the RMSE.
To further validate the PAA_ANFIS, forecasting results are then compared with linear regression. The comparative results show the superiority and higher accuracy achieved by applying the ANFIS, which is equipped with the capability of generating linear relationship and the fuzzy inference system in input-output data. However, it should be noted that an increase in the number of membership functions (MF) will increase the system response time. J. Shafi (&) Á P. pk P. uk P. Angelov Chair of Excellence, Carlos III University, Madrid, Spain J.