Structure Identification of Dynamical Takagi-Sugeno Fuzzy Models by Using LPV Techniques

In this paper the problem of order selection for nonlinear dynamical Takagi-Sugeno (TS) fuzzy models is investigated. The problem is solved by formulating the TS model in its Linear Parameter Varying (LPV) form and applying a recently proposed Regularized Least Squares SupportVector Machine (R-LSSVM) technique for LPV models. In contrast to parametric identification approaches, this non-parametric method enables the selection of the model order without specifying the scheduling dependencies of the model coefficients. Once the correct model order is found, a parametric TS model can be re-estimated by standard methods. Different re-estimation approaches are proposed. The approaches are illustrated in a numerical example

A New Approach to Adaptive Neuro-fuzzy Modeling using Kernel based Clustering

Data clustering is a well known technique for fuzzy model identification or fuzzy modelling for apprehending the system behavior in the form of fuzzy if-then rules based on experimental data Fuzzy c- Means FCM clustering and subtractive clustering SC are efficient techniques for fuzzy rule extraction in fuzzy modeling of Adaptive Neuro-fuzzy Inference System ANFIS In this paper we have employed a novel technique to build the rule base of ANFIS based on the kernel based variants of these two clustering techniques which have shown better clustering accuracy In kernel based clustering approach the kernel functions are used to calculate the distance measure between the data points during clustering which enables to map the data to a higher dimensional space This generalization makes data set more distinctly separable which results in more accurate cluster centers and therefore a more precise rule base for the ANFIS can be constructed which increases the prediction performance of the system The performance analysis of ANFIS models built using kernel based FCM and kernel based SC has been done on three business prediction problems viz sales forecasting stock price prediction and qualitative bankruptcy prediction A performance comparison with the ANFIS models based on conventional SC and FCM clustering for each of these forecasting problems has been provided and discusse

A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems. During the last decade kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify existing kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency and spectral filtering properties. Our theoretical results provide valuable insights in assessing the advantages and limitations of existing pairwise learning methods.Comment: arXiv admin note: text overlap with arXiv:1606.0427