Genetic Optimization of a Self Organizing

In this paper Q self-orgnriizirig firzzy-neiircrI network with a new learning mechanism and rule optimization using genetic ulgoritltin (GA) is proposed fbr locid forecasting l71c number of rules in the inferencing layer is optimized using genetic nlgoritkni and an appropiare fitness jiinction. We devise Q learning dgorithni for. updating the connec~ing weights as well as the structure of the membership functions of the network. The proposed algorithm exploits the notion of error back propagation. The network weights are initialized with random weights inslead of any preselected ones. The performance of the network is validated by extensive sitnulation results using practical data ranging over a period of hvo years. The optiritized frizzy rieirrol nefwork provitlcs on accurate prediction of electrical load in a time frame varying fiom 24 to 168 hours ahead. The algorithm is adaptive and performs much better than the existing ANN techniques used for load forecasting.