نتایج جستجو برای: adaptive nero fuzzy inference system
تعداد نتایج: 2496338 فیلتر نتایج به سال:
Most previous work about the hardware design of a fuzzy logic controller (FLC) intended to either improve its inference performance for real-time applications or to reduce its hardware cost. To our knowledge, there has been no attempt to design a hardware FLC that can perform an adaptive fuzzy inference for the applications of on-line adaptation. The purpose of this paper is to present such an ...
the supplier selection is a multi-criteria decision making which consist both qualities and quantities metrics. a lot of investigations have been published in the supplier selection area and, most of the proposed models focused on manufacturing environments and a few papers have been allocated for service industries. proposed system utilizes new method that transfer the house of quality (hoq) a...
Most of researches on image forensics have been mainly focused on detection of artifacts introduced by a single processing tool. They lead in the development of many specialized algorithms looking for one or more particular footprints under specific settings. Naturally, the performance of such algorithms are not perfect, and accordingly the provided output might be noisy, inaccurate and only pa...
This paper presents an optimal load balancing algorithm based on both of the ANFIS (Adaptive Neuro-Fuzzy Inference System) modeling and the FIS (Fuzzy Inference System) for the local status of real servers. It also shows the substantial benefits such as the removal of loadscheduling overhead, QoS (Quality of Service) provisioning and providing highly available servers, provided by the suggested...
In this paper, we propose a technique of artificial intelligence called adaptive neuro fuzzy inference system (ANFIS) for canceling maternal electrocardiogram (MECG) in fetal electrocardiogram extraction (FECG).This technique is used to estimate the MECG present in the abdominal signal of a pregnant woman. The FECG is then extracted by subtracting the estimated MECG from the abdominal signal. P...
In the current study two methods are evaluated for predicting the compressive strength of concrete containing metakaolin. Adaptive neuro-fuzzy inference system (ANFIS) model and stepwise regression (SR) model are developed as a reliable modeling method for simulating and predicting the compressive strength of concrete containing metakaolin at the different ages. The required data in training an...
This study addresses the proposition of neural network (NN) adaptive control for a class of nonlinear systems using fuzzy reasoning. In first step, an ideal control law is established based on feedback linearization technique and certainty equivalence. Then the NN system is introduced on line to approximate this ideal control law. The parameters of the NN system are on-line adapted and changed ...
in this study, a new adaptive controller is proposed for position control of pneumatic systems. difficulties associated with the mathematical model of the system in addition to the instability caused by pulse width modulation (pwm) in the learning-based controllers using gradient descent, motivate the development of a new approach for pwm pneumatics. in this study, two modified feedback error l...
limitations on freshwater resources have caused water resources managers to focus an increasing attention over the past few decades on water quality protection. surface water quality management in such resources as rivers, seas, lakes, and estuaries is of a greater importance than other water resources and a greater number of studies have been conducted on them as they are more accessible and, ...
This paper presents a new general purpose neuro-fuzzy controller to realize adaptive-network-based fuzzy inference system (ANFIS) architecture. ANFIS which tunes the fuzzy inference system with a back propagation algorithm based on collection of input-output data makes fuzzy system to learn. To implementing this idea we propose several improved CMOS analog circuits, including Gaussian-like memb...
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