Bayesian Learning based Gaussian Approximation for Artificial Neural Networks

Authors : Ozan KOACADAGLİ

Pages : 54-65

View : 34 | Download : 10

Publication Date : 2017-12-29

Article Type : Research Paper

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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 data set.
Keywords : Bayesian Neural Networks, Bayesian Learning, Gaussian Approach, Fixed Hyperparameters, Gradient based Algorithms

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