Robust Control for Universal Learning Network Considering Fuzzy Criterion and Second Order Derivatives

2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)

2012

Nowadays, biopolymer has been actively used in two important areas in our daily activities; packaging and medical devices. One of the important criteria in production of biopolymer is the quality of the polymer. Therefore, a method of controlling biopolymer quality (i.e. molecular weight) is certainly indispensable in this matter. Moreover, biopolymerization is a nonlinear process that requires a powerful tool to tackle the nonlinearity of the process. Neural network is a powerful tool especially in modeling nonlinear and intricate process. Therefore, neural network had been chosen the tool to tackle this task. This work presented a prediction of biopolymer quality using neural networks with bootstrap re-sampling method. Based on the results, neural network provides an alternative path in order to predict biopolymer molecular weight.

Section: Introductionmentioning

confidence: 99%

Biopolycaprolactone molecular weight prediction based on neural network technique in a batch reactor

Noor

2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)

2012

“…In order to improve the robustness of neural networks a number of techniques have been developed lately like regularization [9] and the early stopping method [10]. Ohbayashi [11] implemented the universal learning rule and second order derivatives to increase the robustness in neural network models. Robustness is enhanced by minimizing the change in the values of criterion function caused by the small changes around nominal values of system parameters.…”

Section: Introductionmentioning

confidence: 99%

Optimum Numbers of Single Network for Combination in Multiple Neural Networks Modeling Approach for Modeling Nonlinear System

Noor²

2012

IIUMEJ

This paper is focused on finding the optimum number of single networks in multiple neural networks combination to improve neural network model robustness for nonlinear process modeling and control. In order to improve the generalization capability of single neural network based models, combining multiple neural networks is proposed in this paper. By studying the optimum number of network that can be combined in multiple network combination, the researcher can estimate the complexity of the proposed model then obtained the exact number of networks for combination. Simple averaging combination approach is implemented in this paper which is applied to nonlinear process models. It is shown that the optimum number of networks for combination can be obtained hence enhancing the performance of the proposed model.

“…[5,12]). Reference [13] implemented a universal learning rule with second-order derivatives to increase the robustness in neural network models.…”

Section: Introductionmentioning

confidence: 99%

Bayesian selective combination of multiple neural networks for improving long-range predictions in nonlinear process modelling

Zhang

2004

Neural Comput & Applic

A Bayesian selective combination method is proposed for combining multiple neural networks in nonlinear dynamic process modelling. Instead of using fixed combination weights, the probability of a particular network being the true model is used as the combination weight for combining that network. The prior probability is calculated using the sum of squared errors of individual networks on a sliding window covering the most recent sampling times. A nearest neighbour method is used for estimating the network error for a given input data point, which is then used in calculating the combination weights for individual networks. Forward selection and backward elimination are used to select the individual networks to be combined. In forward selection, individual networks are gradually added into the aggregated network until the aggregated network error on the original training and testing data sets cannot be further reduced. In backward elimination, all the individual networks are initially aggregated and some of the individual networks are then gradually eliminated until the aggregated network error on the original training and testing data sets cannot be further reduced. Application results demonstrate that the proposed techniques can significantly improve model generalisation and perform better than aggregating all the individual networks.