Direct Neural Network Approach Research Articles

An Improved Direct Neural Network Approach to Flatness Pattern Recognition Baseed on GA-RBF

In this paper, an improved the radial basis function (RBF) neural network direct recognition approach to shape flatness pattern is proposed. The genetic algorithm (GA) is employed to obtain more optimal structure and initial parameters of RBF network. The new approach with the advantages of RBF, such as fast learning and high accuracy, is efficient and intelligent. it can not only effectively settle the problem of the different topologic configurations with changing strip widths but also improve practicability and precision.Compared to the improved direct recognition method with GA-BP,The simulation results show that the speed and accuracy of the flatness pattern recognition model based on GA-RBF are obviously improved.

Combined use of partial least-squares regression and neural network for residual life estimation of large generator stator insulation

Residual life estimation of insulation material is always a difficult problem in electrical insulation condition assessment. This paper describes the development of hybrid framework-based systems to estimate the residual life of large generator stator winding insulation using routinely measured non-destructive diagnostic data. Nonlinearity of the stator winding insulation degraded process makes it difficult to establish the relationship between the residual life and the nondestructive parameters by diagnostic data. By using the universal approximation property of neural networks, the PLS modelling method was used to generalize a nonlinear framework. RBF networks were introduced to the PLS modelling in this study, termed ANN-PLS hybrid modelling. A nonlinear modelling task was therefore decomposed into linear outer relations and simple nonlinear inner relations, which was performed by a number of single-input–single-output networks. Since only a small-size network was trained at one time, the over-parametrized problem of the direct neural network approach was circumvented even when the training data were very sparse. As an application, the proposed ANN-PLS hybrid soft computing method was compared with the standard linear PLS and the multiple linear regression. Test results demonstrated that the hybrid approach had better prediction ability than traditional estimation methods.

Combined thresholding and neural network approach for vein pattern extraction from leaf images

Living plant recognition based on images of leaf, flower and fruit is a very challenging task in the field of pattern recognition and computer vision. There has been little work reported on flower and fruit image processing and recognition. In recent years, several researchers have dedicated their work to leaf characterisation. As an inherent trait, leaf vein definitely contains the important information for plant species recognition despite its complex modality. A new approach that combines a thresholding method and an artificial neural network (ANN) classifier is proposed to extract leaf veins. A preliminary segmentation based on the intensity histogram of leaf images is first carried out to coarsely determine vein regions. This is followed by a fine segmentation using a trained ANN classifier with ten features extracted from a window centred on the object pixel as its inputs. Compared with other methods, experimental results show that this combined approach is capable of extracting more accurate venation modality of the leaf for the subsequent vein pattern classification. The approach can also reduce the computing time compared with a direct neural network approach.

Computational intelligence to model the export behavior of multinational corporation subsidiaries in Malaysia

AbstractThe academic literature suggests that the extent of exporting by multinational corporation subsidiaries (MCSs) depends on their strategic role in the multinational corporation (MNC), their age and size, and whether their products are targeted at niche or commodity markets. In particular, it is claimed that MNCs seek to invest in a particular country if its resources adopt a vertically integrated structure, if the country grants regional or global sales mandates to their subsidiaries, or if it has been established in a host market for a longer time and is thus more likely to promote subsidiary exports. Our aim in this article is to model the complex export pattern behavior of multinational corporation subsidiaries in Malaysia using a Takagi–Sugeno fuzzy inference system. The proposed fuzzy inference system (FIS) is optimized by using neural network learning and evolutionary computation. Empirical results clearly show that the proposed approach could model the export behavior reasonably well compared to a direct neural network approach.

Nonlinear PLS modeling using neural networks

This paper discusses the embedding of neural networks into the framework of the PLS (partial least squares) modeling method resulting in a neural net PLS modeling approach. By using the universal approximation property of neural networks, the PLS modeling method is genealized to a nonlinear framework. The resulting model uses neural networks to capture the nonlinearity and keeps the PLS projection to attain robust generalization property. In this paper, the standard PLS modeling method is briefly reviewed. Then a neural net PLS (NNPLS) modeling approach is proposed which incorporates feedforward networks into the PLS modeling. A multi-input-multi-output nonlinear modeling task is decomposed into linear outer relations and simple nonlinear inner relations which are performed by a number of single-input-single-output networks. Since only a small size network is trained at one time, the over-parametrized problem of the direct neural network approach is circumvented even when the training data are very sparse. A conjugate gradient learning method is employed to train the network. It is shown that, by analysing the NNPLS algorithm, the global NNPLS model is equivalent to a multilayer feedforward network. Finally, applications of the proposed NNPLS method are presented with comparison to the standard linear PLS method and the direct neural network approach. The proposed neural net PLS method gives better prediction results than the PLS modeling method and the direct neural network approach.