This paper introduces comprehensive fast control characteristics and continuous compensation means using Adaptive Neuro-Fuzzy Inference System. Flexible AC Transmission System (FACTS) devices have been investigated and adopted in power engineering area. There are so many advantages in using FACTS devices. It can increase dynamic stability, loading capability of transmission lines, improve power...

We use Active Queue Management (AQM) strategy for congestion avoidance in Transmission Control Protocol (TCP) networks to regulate queue size close to a reference level. In this paper we present two efficient and new AQM systems as a queue controller. These methods are designed using Improved Neural Network (INN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Our aim is low queue variation...

We examine various and different approaches for the prediction of economic crisis periods of US economy. We examine the traditional econometric discrete choice Logit and Probit models then a feed-forward neural network (FFNN) model and finally we apply an Adaptive Neuro-Fuzzy Inference System (ANFIS). We examine the period 1950-2009, where we take as the in-sample or training period 1950-2005, ...

In this work neural and neuro-fuzzy controllers are developed for the inverters of Uninterruptible Power Supplies (UPS) to improve their transient response and adaptability to various loads. Idealized load-currentfeedback controller is built to obtain example patterns for training the networks. Example patterns under various loading conditions are used in the off-line training of the selected n...

The paper presents a study upon the possibility to use adaptive-network-based fuzzy inference method (ANFIS) in the identification of distributed parameter systems, implementing a distributed sensor network in the system. Some main properties of different identification methods are presented with possible application. The fuzzy systems, implemented using rule bases, fuzzy values, membership fun...

the late detection of the kick (the entrance of underground fluids into oil wells) leads to oil wellblowouts. it causes human life loss and imposes a great deal of expenses on the petroleum industry.this paper presents the application of adaptive neuro-fuzzy inference system designed for an earlierkick detection using measurable drilling parameters. in order to generate the initial fuzzy infere...

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.07.076 ⇑ Corresponding author. E-mail address: [email protected] (D. Petković) It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult to analyze with conventional analytical methods. Here, a novel design of an ...

Wind generation is hectic by nature, making wind power forecasting highly challenging, particularly for short time frames. Forecasting of wind power is becoming progressively more important to power system operators and electricity market.Wind power is variable and irregular over various timescales as it is weather dependent. Thus precise forecasting of wind power is acknowledged as a major con...

A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the tested on NASDAQ stock market indices which among most widely followed United States. Empirical results show that by determining parameters ANFIS (premise and consequent parameters) using GA, we can improve performance terms Mean...

toxicity of 38 aliphatic carboxylic acids was studied using non-linear quantitative structure-toxicityrelationship (qstr) models. the adaptive neuro-fuzzy inference system (anfis) was used to construct thenonlinear qstr models in all stages of study. two anfis models were developed based upon differentsubsets of descriptors. the first one used log ow k and lumo e as inputs and had good predicti...