Prediction of MPEG-coded video source traffic using recurrent neural networks
IEEE Transactions on Signal Processing
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tuning certainty factor and local weight of fuzzy production rulesby using fuzzy neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Fuzzy neural network with general parameter adaptation for modeling of nonlinear time-series
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An Approach to Estimating Product Design Time Based on Fuzzy -Support Vector Machine
IEEE Transactions on Neural Networks
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When neural networks are used to forecast short-term power load, it can learn the experience by training and generate mapping rules, but these rules are not directly understood in the network. By using the method of integrating neural networks and fuzzy logic, neural networks only settle historical load information. Moreover, fuzzy logic considers the factors which have great effect to load varying, such as air temperature and holidays, etc. According to the own characteristics of short-term load, the membership function are constructed, and the modifying of basic load heft is realized, which can enhance the load forecasting results veracity to a certain extent.