This study addresses the problem of modeling physiological systems using fuzzy logic. As an application we consider the problem of modeling the anesthetics process. In particular we consider the problem of modeling the evolution of the bispectral index in patients undergoing anesthesia with intravenous propofol. An identification based on fuzzy inference system is proposed. One of the main adva...

An algorithm for adaptively controlling genetic algorithm parameter (GAP) coding using fuzzy rules is presented. The fuzzy GAP coding algorithm is compared to the dynamic parameter encoding scheme proposed by Schraudolph and Belew. The performance of the algorithm on a hydraulic brake emulator parameter identification problem is investigated. Fuzzy GAP coding control is shown to dramatically in...

The aim of this article is to present a new approach to machine learning (precisely in classification problems) in which the use of fuzzy logic has been taken into account. We intend to show that fiazzy logic introduces new elements in the identification process, mainly due to the facility to manage imprecise information. An inductive algorithm generating a set of fuzzy rules identifying the sy...

This paper presents a theoretical proposal of fuzzy instrumental variable (FIV) methods for nonlinear discrete time systems identification and parameter estimation in noisy environment based on the fuzzy instrumental variable concept. The theoretical analysis is presented using a suitable formulation associated to Takagi-Sugeno (TS) fuzzy model. The complexity of the algorithm is quite low and ...

By combining the fuzzy theory and neural network technology, a fuzzy neural network (FNN) is proposed in this paper, whose learning algorithms are developed by steep algorithm. The excitation system model based on FNN is also derived in this paper, which can be used for on-line and off-line analysis and control respectively. The simulation results demonstrate that the FNN models can give precis...

In this work, an approach for the development of adaptive fuzzy models is presented. The approach allows to incorporate the system dynamics into the fuzzy membership functions which are defined in terms of a dynamic function with adjustable parameters. These parameters are adapted using a gradient descent based algorithm. Some application examples to illustrate the performance of the dynamical ...

In this paper we present a method of hybrid predictive control (HPC) based on a fuzzy model. The identification methodology for a nonlinear system with discrete state-space variables based on combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for HPC design, where the optimization problem is solved by the use of genetic algorithms (GAs). An illustra...

An automatic neuro-fuzzy rule generation scheme is proposed for backing up navigation of carlike mobile robots. The proposed method is based on the Conditional Fuzzy C-Means (CFCM) and Fuzzy Equalization (FE) methods. The CFCM is adopted to render clusters, which can represent the homogeneous properties of the given input and output fuzzy data, and also the FE method is used to systematically c...

Introduction: One of the essential and critical elements for efficient and effective management of emergencies is anticipation and identification of possible types of emergencies. As such, a framework for anticipating and identifying emergencies was designed and tested in two process industries in the form of a case study. Material and Methods: At first, methods for identifying emergency prepa...