We present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similari...

Fuzzy adaptive equalizers (FAEs) are adaptive equalizers that apply the concepts of fuzzy logic. The main merit of applying FAEs in powerline channel equalization is that linguistic information (fuzzy IF–THEN rules) and numerical information (input–output pairs) can be combined into the equalizers. The adaptive algorithms adjust the parameters of the membership functions which characterize the ...

A weighted linear regression model with impercise response and p-real explanatory variables is analyzed. The LR fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. A least square solution for estimating the parameters of the model is derived. The result are illustrated by the means of some case studies.

Automotive engine air-ratio plays an important role of emissions and fuel consumption reduction while maintains satisfactory engine power among all of the engine control variables. In order to effectively control the air-ratio, this paper presents a model predictive fuzzy control algorithm based on online least-squares support vector machines prediction model and fuzzy logic optimizer. The prop...

This report details conditions under which the Functional Convolution Model described in Asencio et al. (2013) can be identified from Ordinary Least Squares estimates without either dimension reduction or smoothing penalties. We demonstrate that if the covariate functions are not spanned by the space of solutions to linear differential equations, the functional coefficients in the model are uni...

This paper investigates the use of a fuzzy method as a tool for model identification of a non linear and multivariable system when the measurement data is available. In fact, the use of fuzzy clustering facilitates automatic generation of Takagi-Sugeno rules and its antecedent parameters. After the determination of the consequent parameters, these are adapted by a recursive least squares algori...

In the Capital Asset Pricing Model (CAPM), beta coefficient is a very important parameter to be estimated. The most commonly used estimating methods are the Ordinary Least Squares (OLS) and some Robust Regression Techniques (RRT). However, these traditional methods make strong as sumptions which are unrealistic. In addition, The OLS method is very sensitive to extreme observations, while the RR...

Extracting binary strings from real-valued biometric templates is a fundamental step in template compression and protection systems, such as fuzzy commitment, fuzzy extractor, secure sketch, and helper data systems. Quantization and coding is the straightforward way to extract binary representations from arbitrary real-valued biometric modalities. In this paper, we propose a pairwise adaptive p...

Abstract — This paper is devoted to the mathematical analysis and the numerical solution of the problem of designing fuzzy controllers. We show that for a special class of controllers (socalled Sugeno controllers), the design problem is equivalent to a nonlinear least squares problem, which turns out to be ill-posed. Therefore we investigate the use of regularization methods in order to obtain ...