Neural and neuro-fuzzy models are powerful nonlinear modelling tools. Different structures, with different properties, are widely used to capture static or dynamical nonlinear mappings. Static (non-recurrent) models share a common structure: a nonlinear stage, followed by a linear mapping. In this paper, the separability of linear and nonlinear parameters is exploited for completely supervised ...

The past few years have witnessed a growing recognition of soft computing technologies that underlie the conception, design and utilization of intelligent systems. According to Zadeh [1], soft computing consists of artificial neural networks, fuzzy inference systems, approximate reasoning and derivative free optimization techniques. In this paper, we report a performance analysis among Multivar...

Gene expression profiles generated by the highthroughput microarray experiments are usually in the form of large matrices with high dimensionality. Unfortunately, microarray experiments can generate data sets with multiple missing values, which significantly affect the performance of subsequent statistical analysis and machine learning algorithms. Numerous imputation algorithms have been propos...

Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MAR...

Four different approaches to robust fuzzy clustering of time series are presented and compared with respect other existent approaches. These useful cluster when outlying values found in these series, which is often the rule most real data applications. A representation by using B-splines considered and, later, methods applied on fitted coefficients. Feasible algorithms for implementing methodol...

— Fuzzy techniques have been originally invented as a methodology that transforms the knowledge of experts formulated in terms of natural language into a precise computerimplementable form. There are many successful applications of this methodology to situations in which expert knowledge exist, the most well known is an application to fuzzy control. In some cases, fuzzy methodology is applied e...

In order to estimate the response of biometric variables in different irrigation depths radish crop, as well their relations development a fuzzy mathematical analysis was carried out from with percentages crop evapotranspiration (ETc), using Gaussian pertinence functions for input variable and triangular output variables. Validations were performed neural network models, smoothing splines polyn...

Recent articles of Sánchez and Gómez (2003a, 2003b, 2004) addressed the subject of fuzzy regression (FR) and the term structure of interest rates (TSIR). Following Tanaka et. al. (1982), their regression models included a fuzzy output, fuzzy coefficients and an non-fuzzy input vector. The fuzzy components were assumed to be triangular fuzzy numbers (TFNs). The basic idea was to minimize the fuz...

Purpose Existing shape-based fuzzy clustering algorithms are all designed to explicitly segment regular geometricallyshaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily-shaped objects. Design/Methodology/Approach – With the aim of separating arbitrary shaped objects in an image, this paper presents a new detection and separation of gener...