This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002-2012) to explore how various NFS methodologies have been developed during this period. Based on the selected journals of different NFS applications and different online database of NFS, this article surveys and classifies NFS applications into ten different categories such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, NFS enhancements and social sciences. For each of these categories, this paper mentions a brief future outline. This review study indicates mainly three types of future development directions for NFS methodologies, domains and article types: (1) NFS methodologies are tending to be developed toward expertise orientation. (2) It is suggested that different social science methodologies could be implemented using NFS as another kind of expert methodology. (3) The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.

https://romisatriawahono.net/lecture/rm/survey/softcomputing/Kar%20-%20Neuro%20Fuzzy%20Systems%20-%202014.pdf

Review Article: Applications of neuro fuzzy systems: A brief review and future outline

This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

/pdf/application-of-big-data-and-machine-learning-in-smart-grid-1taystle2u.pdf

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

Data analytics are now playing a more important role in the modern industrial systems. Driven by the development of information and communication technology, an information layer is now added to the conventional electricity transmission and distribution network for data collection, storage and analysis with the help of wide installation of smart meters and sensors. This paper introduces the big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as huge amount of data collection are firstly discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of the typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this paper. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., many benefits can be brought to the existing power system and improve the customer service as well as the social welfare in the era of big data. However, to advance the applications of the big data analytics in real smart grids, many issues such as techniques, awareness, synergies, etc., have to be overcome.

/pdf/big-data-analytics-in-smart-grids-a-review-4g4lfx70ug.pdf

Big data analytics in smart grids: a review

An artificial neural network (ANN) model was used to estimate the solar radiation parameters for seven cities from Mediterranean region of Anatolia in Turkey. As well known that Turkey is a bridge between Asia and Europe and it lies in a sunny belt, between 36^o and 42^oN latitudes. Indeed, the country has sufficient solar radiation intensities for solar applications. In order to make estimation of solar radiation, the data from the Turkish State and Meteorological Service were used. Data of 2006 were used for testing and data of 2005, 2007, and 2008 were estimated. Effects of number of input parameters were tested on solar radiation that was output layer. With this aim, number of input layer parameters changed from 2 to 6. The obtained results indicated that the method could be used by researchers or scientists to design high efficiency solar devices. It was also found that number of input parameters was the most effective parameter on estimation of future data on solar radiation.

Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey

Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature

Monitoring Power Quality Events (PQE) is a crucial task for sustainable and resilient smart grid. This paper proposes a fast and accurate algorithm for monitoring PQEs from a pattern recognition perspective. The proposed method consists of two stages: feature extraction (FE) and decision-making. In the first phase, this paper focuses on utilizing a histogram based method that can detect the majority of PQE classes while combining it with a Discrete Wavelet Transform (DWT) based technique that uses a multi-resolution analysis to boost its performance. In the decision stage, Extreme Learning Machine (ELM) classifies the PQE dataset, resulting in high detection performance. A real-world like PQE database is used for a thorough test performance analysis. Results of the study show that the proposed intelligent pattern recognition system makes the classification task accurately. For validation and comparison purposes, a classic neural network based classifier is applied.

Power Quality Event Detection Using a Fast Extreme Learning Machine

In this paper, a fuzzy neural network (FNN) controller which emulates the conventional IP controller is proposed for a vector controlled induction motor drive. A Sugeno type FNN is adopted for the proposed control system and the fuzzy neural network is so designed that the FNN controller behaves an robust-nonlinear IP controller. The proposed FNN-IP controller is used for the position control of induction motor drive and the performance and the robustness of the control system is tested for nonlinear motor loads and parameter variations. The FNN-IP controller is trained off-line using experimental data's and then the trained controller is used for experimental studies. DS1104 digital signal processor control card is used to implement the control algorithm. Experimental results showing the effectiveness of the proposed control system are presented for parameter and load variations of the motor.

Fuzzy neural network IP controller for robust position control of induction motor drive

Design and implementation of Type-2 fuzzy neural system controller for PWM rectifiers

Neural and fuzzy courses are widely offered at graduate and undergraduate level due to the successful applications of neural and fuzzy control to nonlinear and unmodeled dynamic systems, including electrical drives. However, teaching students a neuro-fuzzy controlled electrical drive in a laboratory environment is often difficult for schools with limited access to expensive equipment facilities. Therefore, computer simulations and dedicated software are needed to assist the students in visualizing the concepts and to provide graphical feedback during the learning process. In this article, an educational software is proposed for the neuro-fuzzy control of induction machine drives. The tool helps students learn the application of neuro-fuzzy control of electrical drives. The software has a flexible structure and graphical user interface. The neuro-fuzzy architecture, the motor and load parameters can be easily changed in the developed software. Neuro-fuzzy control performance of induction motors can be monitored graphically for various control structures and current controllers Comput Appl Eng Educ 14: 211–221, 2006; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20082

A virtual electrical drive control laboratory: Neuro‐fuzzy control of induction motors

In this paper, a hybrid neuro-fuzzy controller (NFC) is presented for the speed control of brushless DC motors to improve the control performance of the drive under transient and steady state conditions In the hybrid control system, proportional-derivative (PD) type neuro-fuzzy controller (NFC) is the main tracking controller, and an integral compensator is proposed to compensate the steady state errors A simple and smooth activation mechanism described for integral compensator modifies the control law adaptively The presented BLDC drive has fast tracking capability, less steady state error and robust to load disturbance, and do not need complicated control method Experimental results showing the effectiveness of the proposed control system are presented

Besir Dandil

Papers

Power Quality Event Detection Using a Fast Extreme Learning Machine

Fuzzy neural network IP controller for robust position control of induction motor drive

Design and implementation of Type-2 fuzzy neural system controller for PWM rectifiers

A virtual electrical drive control laboratory: Neuro‐fuzzy control of induction motors

A hybrid neuro-fuzzy controller for brushless DC motors