Multilayer feedforward networks are universal approximators
Neural Networks
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Predicting a chaotic time series using a fuzzy neural network
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A hybrid machine learning method and its application in municipal waste prediction
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Hi-index | 12.05 |
Both planning and design of municipal solid waste management systems (MSWMS) require accurate prediction of waste generation (WG). In this study, the hybrid of wavelet transform-adaptive neuro-fuzzy inference system (WT-ANFIS) and wavelet transform-artificial neural network (WT-ANN) is used to predict the weekly WG in Tehran, concerning complexity and dynamic MSWMS. In order to input variables preprocessing is done by WT and then new variables entered to ANFIS and ANN models. Consequently, output uncertainty of WT-ANFIS and WT-ANN models is done. The results achieved in this research indicate the positive effect of input variables preprocessing by WT in the prediction of weekly WG in Tehran, and it has led to noticeable increase in the accuracy of two model calculations. However, WT-ANFIS model had better results than WT-ANN model, because of the smaller uncertainty than WT-ANN model.