Search results for "Inference system"
showing 10 items of 34 documents
A genetic integrated fuzzy classifier
2005
This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.
A sizing approach for stand-alone hybrid photovoltaic-wind-battery systems: A Sicilian case study
2018
Abstract Solar and wind energy are the two most available renewable energy resources in the world. In this paper, a high-resolution analysis that allows sizing a hybrid photovoltaic-wind turbine-battery banks has been carried out. The analysis aims to minimize the annualized cost of the systems satisfying two reliability constraints. The solution has been obtained numerically by means of an iterative technique. The decision variables are the photovoltaic area, wind turbine radius, and battery capacity. A high-resolution model, based on fuzzy logic inference system, has been developed to evaluate the number of active occupants and the domestic electricity consumption. In order to allow a mor…
A new fuzzy robust dynamic controller for autonomous vehicles with nonholonomic constraints
2005
Abstract In this paper a novel algorithm with a dynamic fuzzy controller applied to the control of trajectory of vehicles with two independent wheels is proposed. An automatic control of trajectory of a vehicle can behave in a not efficient way. It is necessary to consider the friction of the actuators and possible perturbations coming from the outside environment, as for instance the variable characteristics of the ground where the vehicle moves. These perturbations, which depend also on the contact between the wheel and the ground, involve violations of nonholonomic constraints. Thus it is necessary to compensate for these perturbations to obtain a robust control system. The controller sy…
Control of uncertain highly nonlinear biological process based on Takagi–Sugeno fuzzy models
2015
This note deals with the control of uncertain highly nonlinear biological processes. Indeed, an adaptive fuzzy control (AFC) scheme is developed for the pre-treatment of wastewater represented by a Takagi-Sugeno (TS) fuzzy model. The proposed approach uses a fuzzy system to approximate the unknown substrate consumption rate in designing the adaptive controller, and then an observer is designed to estimate the concentration in substrate at the outlet bioreactor. The observer is employed to generate an error signal for the adaptive control law which permits to minimize the influence of the measurement noise on the estimation of the substrate concentration. An update of the fuzzy models parame…
The facility layout problem approached using a fuzzy model and a genetic search
2005
The problem of facility layout design is discussed, taking into account the uncertainty of production scenarios and the finite production capacity of the departments. The uncertain production demand is modelled by a fuzzy number, and constrained arithmetic operators are used in order to calculate the fuzzy material handling costs. By using a ranking criterion, the layout that represents the minimum fuzzy cost is selected. A flexible bay structure is adopted as a physical model of the system while an effective genetic algorithm is implemented to search for a near optimal solution in a fuzzy contest. Constraints on the aspect ratio of the departments are taken into account using a penalty fun…
A SYNTHETIC MEASURE FOR THE ASSESSMENT OF THE PROJECT PERFORMANCE
2009
The present paper aims to offer a synthetic project performance indicator (PPI) that aggregates two input parameters obtained by the Earned Value Analysis. The PPI is calculated by using a Fuzzy Inference System (FIS) able to single out a measure based on the input parameters, instead of formulating a mathematical model that could be a troublesome task whenever complex relations among the input variables exist. The purpose is to communicate the project performance to the stakeholders in a clear and complete way, for example, describing the PPI by means of contour lines.
Non-fragile fuzzy control design for nonlinear time-delay systems
2013
In this paper, a non-fragile fuzzy control design is proposed for a class of nonlinear systems with mixed discrete and distributed time delays. The Takagi and Sugeno (T-S) fuzzy set approach is applied to the modelling of the nonlinear dynamics, and a T-S fuzzy model is constructed, which can represent the nonlinear system. Then, based on the fuzzy linear model, a fuzzy linear controller is developed to stabilize the nonlinear system. The control law is obtained to ensure stochastically exponentially stability in the mean square. The sufficient conditions for the existence of such a control are proposed in terms of certain linear matrix inequalities.
Adaptive type-2 fuzzy control of non-linear systems
2009
The paper describes the development of two different type-2 adaptive fuzzy logic controllers and their use for the control of a non linear system that is characterized by the presence of bifurcations and parameter uncertainty. Although a type-2 fuzzy logic controller is able to handle the non linearities and the uncertainties present in a system, its robustness and effectiveness can be increased by the use of an opportune adaptive algorithm. A simulation study was conducted to compare the behavior of adaptive controllers with that of simple type-1 and type-2 fuzzy logic controllers. The system to be controlled, used for the simulation, is a continuous bioreactor for the treatment of mixed w…
Adaptive type-2 fuzzy logic control of a bioreactor
2010
Two adaptive type-2 fuzzy logic controllers with minimum number of rules are developed and compared by simulation for control of a bioreactor in which aerobic alcoholic fermentation for the growth of Saccharomyces cerevisiae takes place. The bioreactor model is characterized by nonlinearity and parameter uncertainty. The first adaptive fuzzy controller is a type-2 fuzzy-neuro-predictive controller (T2FNPC) that combines the capability of type-2 fuzzy logic to handle uncertainties, with the ability of predictive control to predict future plant performance making use of a neural network model of the nonlinear system. The second adaptive fuzzy controller is instead a self-tuning type-2 PI cont…
Nonlinear fuzzy control of a fed-batch reactor for penicillin production
2012
Abstract The process of penicillin production is characterized by nonlinearities and parameter uncertainties that make it difficult to control. In the paper the development and testing of a multivariable fuzzy control system that makes use of type-2 fuzzy sets for the control of pH and temperature are described. The performance of the type-2 fuzzy logic control system (T2FLCS) is compared by simulation with that of a type-1 fuzzy logic control system (T1FLCS) and that of a control system with traditional proportional-integral-derivative (PID) controllers proposed in the literature. The fuzzy controllers are optimized using an ANFIS algorithm. The best results are obtained with the T2FLCS pa…