Search

In this study, we introduce novel fuzzy neural networks designed with the aid of linear function prototype-driven fuzzy clustering (LFPFC) and layer reconstruction-based network design strategy to deal with the regression problem. The LFPFC constitutes a new clustering technique inspired by the fuzzy c-regression model (FCRM) clustering unlike fuzzy c-means (FCM) clustering LFPFC represents the prototypes of clusters as linear functions, and this can lead to more reliable data analysis of complex regression problems. We propose two types of LFPFC such as an estimated output-based LFPFC and a distance-based LFPFC. The estimated output-based LFPFC uses the output estimated on a basis of the simple model instead of the target output to calculate the centroid of LFPFC. A centroid of distance-based LFPFC is computed through the Euclidean distance between input data and the centroid of the cluster. By using two kinds of LFPFC approaches, we propose three different types of fuzzy neural networks: i) the fuzzy neural networks through layer reconstruction-based network design strategy consists of two models. The first model serves as an estimate of the desired output and the estimated output is used in the LFPFC of the second model. ii) In the fuzzy neural networks applied to the basic architecture of distance-based LFPFC, the hidden layer using the membership function changes to basic distance-based LFPFC, and the partition matrix obtained from LFPFC is used as the output of the hidden layer. iii) in the fuzzy neural network with the advanced architecture of distance-based LFPFC, an additional auxiliary layer is considered between the hidden and output layers to estimate the membership function of output space through LFPFC. In the experiments, we evaluate the performance index of the proposed models using publicly available machine learning datasets. The superiority of the proposed fuzzy neural networks designed by using LFPFC is demonstrated through the comparative analysis with the diverse regression models offered in the Weka data mining software. By conducting the Friedman test we show that the proposed model exhibits visible competitiveness from the viewpoint of performance. In addition, a real-world Portland cement dataset is dealt with to demonstrate the superiority of the models designed with the aid of LFPFC and reinforced layer reconstruction-based network design strategy. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have attracted increasing attention in recent years because of their powerful abilities to extract and represent spatial/temporal information. However, for general data, its features are assumed to have weak or no correlation, and directly applying CNN to classify such data could result in poor classification performance. To address this problem, a combined technique of original data representation method of fuzzy penalty function-based constrained particle swarm optimization (FCPSO) and CNN, so-called FCPSO-CNN is designed to effectively solve the classification problems for generic dataset and applied to recognize (classify) black plastic wastes in recycling problems. In more detail, CPSO is introduced to optimize feature reordering matrix under constraints and the construction of this matrix is driven by fitness function of CNN that quantifies classification performance. The Mamdani type fuzzy inference system (FIS) is employed to realize the fuzzy penalty function (FPF) which is utilized to realize the constrained problems of CPSO as well as alleviate the issues of the original penalty function method suffering from the lack of robustness. Experimental results demonstrate that FCPSO-CNN achieves the best classification accuracy on 13 out of 17 datasets; the statistical analysis also confirms the superiority of FCPSO-CNN. An interesting point is worth to mention that some feature reordering matrices in the infeasible space come with better classification accuracy. It has been found that the proposed method results in more accurate solution than one-dimensional CNN, random reordering feature-based CNN and some well-known classifiers (e.g., Naive Bayes, Multilayer perceptron, Support vector machine). • CNN design based on the novel data representation method leads to preferred classifier. • CPSO is applied to acquire the available features reordering matrix with spatial information from many matrices. • FPF is employed to copy with CO problem, which combines the FIS to self-adaptively modify penalty parameter. • Visualization technique of t-SNE is applied to the proposed data representation method. • The proposed FCPSO is applied to solve the real-life problem of black plastic wastes. [ABSTRACT FROM AUTHOR]

• The convolution-base and composite kernel are used for alleviating overfitting. • The composite kernel could adjust the nonlinear fitting ability of the classifier. • Hybrid fuzzy multi-(HFM) SVM leads to better results with flexible feature fusion. • The effectiveness of the HFM-SVM is demonstrated by three practical applications. Lately, Convolutional Neural Networks (CNNs) have been introduced to extract features and further enhance the classification performance in different application areas. In this study, a hybrid fuzzy multiple (HFM)-SVM design with the convolution-base (which consists of a series of pooling and convolutional layers) and composite kernel function is proposed. The objective of the proposed classifier is to enhance the nonlinear fitting ability of the classifier and improve the classification performance in high-dimensional applications. The key points of the proposed HFM SVM are enumerated as follows: 1) The convolution-base of the proposed classifier extracts features. The extracted features exhibit flexibility and applicability in high-dimensional applications. 2) The proposed HFM SVM designed with the composite kernel could adjust the nonlinear fitting ability for improving classification performance. The procedure of the proposed HFM SVM is described as follows: Convolution-base is considered as a preprocessing unit for extracting features. The features are not always linearly separable especially when being extracted from high dimensional data. A composite kernel function is constructed by considering the complicated (nonlinear) classification boundary into several local linear boundaries. The structure of the extracted features is captured by FCM clustering and integrated into the composite kernel function for enhancing the nonlinear fitting ability of the proposed classifier. The nonlinearity of the composite function can fill the gap between linear and nonlinear kernel functions by adjusting the number of clusters obtained by the FCM clustering algorithm. The proposed HFM SVM classifier based on composite kernel could improve the classification performance on high dimensional datasets. The performance of the proposed HFM SVM is experimented with and demonstrated by using three high-dimensional applications to show the effectiveness as well as performance improvement. [ABSTRACT FROM AUTHOR]

• AFS and RS are used to effectively obtain the transmittance spectra of black plastic wastes not available in the existing NIR. • A feature selection method of the peak points is considered based on the analysis of chemical information for low cost spectrometer by using a small amount of input variables. • A method of 2D reconstruction through mapping from 1D signal data based on peak points is proposed to effectively deal with spatial structure information between data. • 2DCNN design driven by 2D information features or chemical peak points data leads to high performance as well as anti-noise capability through effective data mapping. In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes. [ABSTRACT FROM AUTHOR]