Bayesian Learning based Gaussian Approximation for Artificial Neural Networks
Journal Title: Turkish Journal of Forecasting - Year 2017, Vol 1, Issue 2
Abstract
In the nonlinear systems, the pre-knowledge about the exact functional structure between inputs and outputs is mostly either unavailable or insufficient. In this case, the artificial neural networks (ANNs) are useful tools to estimate this functional structure. However, the traditional ANNs with the sum squared error suffer from the approximation and estimation errors in the high dimensional and excessive nonlinear cases. In this context, Bayesian neural networks (BNNs) provide a natural way to alleviate these issues by means of penalizing the excessive complex models. Thus, this approach allows estimating more reliable and robust models in the regression analysis, time series, pattern recognition problems etc. This paper presents a Bayesian learning approach based on Gaussian approximation which estimates the parameters and hyperparameters in the BNNs efficiently. In the application part, the proposed approach is compared with the traditional ANNs in terms of their estimation and prediction performances over an artificial dataset.
Authors and Affiliations
Ozan Kocadagli
Keywords
Related Articles
- EP ID EP300043
- DOI -
- Views 152
- Downloads 0
BIST 100 Index Estimation Using Bayesian Regression Modelling
Identification of factors, determining the fluctuations of stock indices in the market, possesses great importance for the capital market actors. Not only specifying the factors and market but also explaining the relatio...
A Fuzzy Modelling Approach to Robust Design via Loss Functions
Especially in a world where industrial development is reinforced by globalization tendencies, competitive companies know that satisfying customers' needs and running a successful operation requires a process that is reli...
Multi-layer Perceptron and Pruning
A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its “black box” aspect, many researchers refuse to use it. Moreover, the optimization (o...
Forecasting of Apricot Production of Turkey by Using Box-Jenkins Method
Turkey is the first largest apricot producer in the world. In 2016, Turkey was responsible for 9,21% of world apricot production with 730 thousand tons. Turkey also generated 11,31% of world apricot exports in 2016. The...
Time Series Prediction with Direct and Recurrent Neural Networks
This article presents a comparative study of the prediction of time series for the Consumer Price Index (CPI) using a recurrent neural network (RNN). For this, three models are designed for recurrent networks, with chang...