We present a new implementation of the commonly used Box-fitting Least Squares (BLS) algorithm, for detection transiting exoplanets in photometric data. Unlike BLS, our - Sparse BLS (SBLS), does not use binning data into phase bins, nor it any kind grid. Thus, its efficiency depend on transit phase, and is therefore slightly better than that BLS. For sparse data, also significantly faster It pe...

This paper investigates the use of evolving fuzzy algorithms in forecasting. An Evolving Takagi-Sugeno algorithm (eTS), which is based on a recursive version of the Subtractive algorithm is considered. It groups data into several clusters based on Euclidean distance between the relevant independent variables. The Mod eTS algorithm, which incorporates a modified dynamic update of cluster radii w...

Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is ...

Fuzzy clusterwise regression has been a useful method for investigating cluster-level heterogeneity of observations based on linear regression. This method integrates fuzzy clustering and ordinary least-squares regression, thereby enabling to estimate regression coefficients for each cluster and fuzzy cluster memberships of observations simultaneously. In practice, however, fuzzy clusterwise re...

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthes...

In many fields where human understanding plays a crucial role, such as bioprocesses, the capacity of extracting knowledge from data is of critical importance. Within this framework, fuzzy learning methods, if properly used, can greatly help human experts. Amongst these methods, the aim of orthogonal transformations, which have been proven to be mathematically robust, is to build rules from a se...

Recently, data driven fuzzy modelling has drawn a great deal of attention not only from academia but also from industry. However, a dynamic fuzzy model might be difficult to develop when training data do not contain sufficient information about the process nonlinearity. In order to avoid this problem and to reduce the model complexity, this paper presents a fuzzy Hammerstein (FH) modelling appr...

In this paper, we propose an iterative algorithm for multiple regression with fuzzy variables.While using the standard least-squares criterion as a performance index, we pose the regression problem as a gradient-descent optimisation. The separation of the evaluation of the gradient and the update of the regression variables makes it possible to avoid undue complication of analytical formulae fo...