A Novel Dynamic Weight Neural Network Ensemble Model
نویسندگان
چکیده
منابع مشابه
A novel neural network ensemble architecture for time series forecasting
We propose a novel homogeneous neural network ensemble approach called Generalized Regression Neural Network (GEFTS–GRNN) Ensemble for Forecasting Time Series, which is a concatenation of existing machine learning algorithms. GEFTS uses a dynamic nonlinear weighting system wherein the outputs from several base-level GRNNs are combined using a combiner GRNN to produce the final algorithm appears...
متن کاملEnsemble strategies to build neural network to facilitate decision making
There are three major strategies to form neural network ensembles. The simplest one is the Cross Validation strategy in which all members are trained with the same training data. Bagging and boosting strategies pro-duce perturbed sample from training data. This paper provides an ideal model based on two important factors: activation function and number of neurons in the hidden layer and based u...
متن کاملNeuramanteau: A Neural Network Ensemble Model for Lexical Blends
The problem of blend formation in generative linguistics is interesting in the context of neologism, their quick adoption in modern life and the creative generative process guiding their formation. Blend quality depends on multitude of factors with high degrees of uncertainty. In this work, we investigate if the modern neural network models can sufficiently capture and recognize the creative bl...
متن کاملA Novel Nonlinear Neural Network Ensemble Model Using K-PLSR for Rainfall Forecasting
In this paper, a novel hybrid Radial Basis Function Neural Network (RBF–NN) ensemble model is proposed for rainfall forecasting based on Kernel Partial Least Squares Regression (K–PLSR). In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input to t...
متن کاملA Novel Feature Extraction-based Selective & Nonlinear Neural Network Ensemble Model for Economic Forecasting
In this study, a novel selective & nonlinear neural network ensemble model, i.e. NSNNEIPCABag, is proposed for economic forecasting. In this model, some different training subsets are first generated by bagging algorithm. Then the feature extraction technique, improved principal component analysis (IPCA), and then the IPCA approach is also used to extract their data features to train individual...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Distributed Sensor Networks
سال: 2015
ISSN: 1550-1477,1550-1477
DOI: 10.1155/2015/862056