197,361 research outputs found
Safety of COVID-19 vaccines in patients with psoriasis undergoing therapy with anti-interleukin agents
No full textIntroduction: There is very limited kn3e safety of COVID-19 vaccines in patients with psoriasis who are being treated with biological agents. We present our experience in 369 patients with moderate-to-severe psoriasis undergoing therapy with anti-IL agents who were vaccinated against SARS-CoV-2. Areas covered: None of the 369 patients referred to any serious adverse event related to vaccination against COVID-19, while about one-third reported mild adverse events similar to those seen in the general population that were resolved within 48 hours. No patient discontinued biological therapy to receive a COVID-19 vaccine. Expert opinion: Our observations provide evidence that COVID-19 vaccines can be considered safe in patients with moderate-to-severe psoriasis who are receiving anti-IL therapy
A design methodology for adaptive type-2 fuzzy controllers
No full textThe main objective of the study is to provide a valid and effective methodology for the design and development of adaptive type-2 fuzzy controllers (AT2FLCs). The methodology is based on an in depth analysis of the process dynamics and the use of an Adaptive Neuro Fuzzy Inference System (ANFIS) technique for the optimization of controllers. The application of the methodology is shown in the design and development of AT2FLCs for two biochemical processes characterized by uncertainty and time varying parameters. The simulation results show that the union of the ANFIS optimization method with the adaptive fuzzy algorithm operating on the output scaling factor, allows to obtain robust AT2FLCs, with fewer rules than traditional type-2 FLCs, that minimize the negative effects of all system parameter changes and achieve in all cases a very high control performance
Experimental Comparison of Type-1 and Type-2 Fuzzy Logic Controllers for the Control of Level and Temperature in a Vessel
No full textThe objective of this experimental study is to compare the performance of type-1 and type-2 fuzzy logic controllers on a real system where the control of liquid level and temperature are considered. By the use of genetic algorithms it is possible to optimize the fuzzy sets of each fuzzy controller assuring high control performance. The experimental results show that a better control in terms of robustness can be achieved by type-2 fuzzy logic controllers
Development of a predicitive type-2 neurofuzzy controller
No full textA controller that combines the main characteristics and advantages of three different control methodologies is proposed for the control of systems with nonlinearities and uncertainties. A neural network predictive control approach is implemented modifying the output of a controller with a fuzzy logic structure that uses type-2 fuzzy sets. Neural networks are also used to optimize the membership function parameters. The proposed controller is tested by simulation for the control of a bioreactor characterized by bifurcation and parameter uncertainty
Adaptive Type-2 Fuzzy Logic Control of Non-Linear Processes
No full textThe main objective of this study is to provide a valid and effective approach for the design and development of an adaptive type-2 fuzzy controller (AT2FLC), based on the analysis of the nonlinear process dynamics and the use of an ANFIS technique for the optimization of the controller. The performance of the obtained AT2FLC, characterized by a few number of rules, is higher than the performance of a traditional type-2 fuzzy controller with a larger rule base. The proposed controller is particurarly suitable for the control of processes characterized by uncertainty and time varying parameters
Type-2 Fuzzy Control of a Bioreactor
No full textAbstract—In this paper the control of a bioprocess using
an adaptive type-2 fuzzy logic controller is proposed.
The process is concerned with the aerobic alcoholic
fermentation for the growth of Saccharomyces Cerevisiae
a n d i s characterized by nonlinearity and parameter
uncertainty. Three type-2 fuzzy controllers heve been
developed and tested by simulation: a simple type-2
fuzzy logic controller with 49 rules; a type-2 fuzzyneuro-
predictive controller (T2FNPC); a t y p e -2 selftuning
fuzzy controller ( T2STFC). The T2FNPC
combines the capability of the 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 non linear system. In
the T2STFC the output scaling factor is adjusted on-line
by fuzzy rules according to the current trend of the
controlled process. T h e advantage of the proposed
adaptive algorithms is to greatly decrease the number of
rules needed for the control reducing the computational
load and at same time assuring a robust control
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