Fuzzy Unit Research Articles

Optimizing Power System Reliability and Carbon Emissions With a Fuzzy Unit Commitment Model Incorporating Renewable Energy, Load Forecast Errors, EV Charging, and Energy Storage System

Optimizing Power System Reliability and Carbon Emissions With a Fuzzy Unit Commitment Model Incorporating Renewable Energy, Load Forecast Errors, EV Charging, and Energy Storage System

MULTIVARIATE FUZZY APPROXIMATION BY NEURAL NETWORK OPERATORS ACTIVATED BY A GENERAL SIGMOID FUNCTION

Here is studied in detail the multivariate fuzzy approximation to the multivariate unit by multivariate fuzzy neural network operators activated by a general sigmoid function. These operators are multivariate fuzzy analogs of earlier studied multivariate Banach space valued ones. The derived results generalize earlier Banach space valued ones into the fuzzy level. Here the high order multivariate fuzzy pointwise and uniform convergences with rates to the multivariate fuzzy unit operator are given through multivariate fuzzy Jackson type inequalities involving the multivariate fuzzy moduli of continuity of the mth order (m ≥ 0) H-fuzzy partial derivatives, of the involved multivariate fuzzy number valued function. The treated operators are of averaged, quasi-interpolation, Kantorovich and quadrature types at the multivariate fuzzy setting.

A type-2 fuzzy rule-based model for diagnosis of COVID-19

In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods in the diagnosis of COVID-19 has been developed. The developed system consists of three fuzzy units. The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests. In the third fuzzy unit, the COVID-19 positivity risks according to the clinical examination and blood analysis results, which are the outputs of the first and second fuzzy units, are evaluated together and the result is obtained. As a result of the evaluation of the trials with 60 different scenarios by physicians, it has been revealed that the system can detect COVID-19 risk with 86.6% accuracy.

ИНТЕЛЛЕКТУАЛЬНАЯ МОДЕЛЬ МАКСИМИЗАЦИИ ГЕНЕРИРУЕМОЙ МОЩНОСТИ РЕКОНФИГУРИРУЕМОЙ СОЛНЕЧНОЙ ЭЛЕКТРОСТАНЦИИ

The global maximum power point tracking of a solar power plant in partial shading demands a global optimization. Standard algorithms for tracking of maximum power point do not provide for a maximum global power of a solar power plant during real time mode due to low convergence. A model of maximizing the generated power of a reconfigurable solar power plant was developed as a modified fuzzy deep neural network based on the modified quantum-behaved particle swarm optimizer. This neural network consists of the following: convolutional units, recurrent neural networks, and fuzzy units. By processing the sensor signals and images of the solar array, the set modified fuzzy deep neural network generates a reference voltage and an electrical interconnection matrix of the parallel-serial solar array, maximizing its power under non-uniform insolation.

Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals

Achieving an efficient and reliable method is essential to interpret a user’s brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it is a significant problem to be addressed in mental task as motor imagery. Therefore, fuzzy components may help to enable a higher tolerance to noisy conditions. With the advent of Deep Learning and its considerable contributions to Artificial intelligence and data analysis, numerous efforts have been made to evaluate and analyze brain signals. In this study, to make use of neural activity phenomena, the feature extraction preprocessing is applied based on Multi-scale filter bank CSP. In the following, the hybrid series architecture named EEG-CLFCNet is proposed which extract the frequency and spatial features by Compact-CNN and the temporal features by the LSTM network. However, the classification results are evaluated by merging the fully connected network and fuzzy neural block. Here, the proposed method is further validated by the BCI competition IV-2a dataset and compare with two hyperparameter tuning methods, Coordinate-descent and Bayesian optimization algorithm. The proposed architecture that used fuzzy neural block and Bayesian optimization as tuning approach, results in better classification accuracy compared with the state-of-the-art literatures. As results shown, the remarkable performance of the proposed model, EEG-CLFCNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced MI-based BCI systems.

Discrete similarity measures on Pythagorean fuzzy sets and its applications to medical diagnosis and clustering problems

Pythagorean fuzzy sets are an extension of intuitionistic fuzzy sets and are more efficient from an application perspective. Though the Pythagorean fuzzy sets are more informative, not much work on similarity measures is available in the literature. Furthermore, existing similarity measures are not efficient. Also, the containment property in Pythagorean fuzzy units is not correctly defined or ineffective. As a result, the existing similarity measures do not reflect appropriate information between the Pythagorean fuzzy sets. The scalar function of the Yager is mainly used for verifying the validity of similarity measures. Most of the existing similarity measures do not conform to the Yager scalar function. Hence, the existing similarity measures exhibit some discrepancies. Furthermore, the existing similarity measures are inconsistent in determining the similarity in intuitionsitic and Pythagorean fuzzy sets. In some real-world modeling issues, past, present, and cross-time information are essential. However, such information is missing in the existing similarity measures. Therefore, in this paper, two new measures of similarity are being developed based on the deviation of the parameters: membership degree, nonmembership degree, strength of commitment, direction of commitment, and cross-time evaluation factors. Under this construction, the proposed similarity measures effectively measure the similarity between the Pythagorean fuzzy sets. Furthermore, the newly defined containment property is also reflected in the proposed similarity measures, which were a limitation in most cases. Moreover, Yager's scalar function is also reflected by the proposed similarity measures. The complement of given information is also essential in some real-world problems. However, such information is incomplete in the theory of Pythagorean fuzzy sets. Hence, the complement of the Pythagorean fuzzy set is being redefined, and a few related results on similarity measures are proposed. Finally, the proposed similarity measures are tested for applicability to medical diagnosis and clustering problems through some hypothetical case studies.

Detection of the Type of Left and Right Returns-to-Scales Using Envelopment DEA Models in Crisp and Fuzzy Environments: An Application for Predicting Changes in the Stock Market

This paper aims to analyze left and right returns-to-scales in crisp and fuzzy data envelopment analysis (DEA). Since all previous envelopment DEA models for assessing left and right returns-to-scales are parametric, they are prone to encountering infeasibility problems, producing incorrect or different solutions for determining the type of returns-to-scale because of the different choices of parameter values. This misdiagnosis will lead to poor management decisions. Due to the mentioned problems, the issue of one-sided returns-to-scale has also not been studied in inaccurate environments. The present paper first proposes an alternative method of left and right returns-to-scales determination with crisp data to address this problem. This approach develops two non-parametric envelopment DEA models for analyzing left and right returns-to-scales. Then, the proposed method is extended to the fuzzy environment where data are considered more realistic. Due to its major advantages, credibility measure is used for solving fuzzy DEA models built to determine left and right returns-to-scales. As an application of the proposed method, data of companies in the Iran stock market are collected for 2014–2019 as fuzzy data and frontier units are analyzed by one-sided returns-to-scale.

Fuzzy temporal convolutional neural networks in P300-based Brain–computer interface for smart home interaction

The processing and classification of electroencephalographic signals (EEG) are increasingly performed using deep learning frameworks, such as convolutional neural networks (CNNs), to generate abstract features from brain data, automatically paving the way for remarkable classification prowess. However, EEG patterns exhibit high variability across time and uncertainty due to noise. It is a significant problem to be addressed in P300-based Brain Computer Interface (BCI) for smart home interaction. It operates in a non-optimal natural environment where added noise is often present. In this work, we propose a sequential unification of temporal convolutional networks (TCNs) modified to EEG signals, LSTM cells, with a fuzzy neural block (FNB), we called EEG-TCFNet. Fuzzy components may enable a higher tolerance to noisy conditions. We applied three different architectures comparing the effect of using block FNB to classify a P300 wave to build a BCI for smart home interaction with healthy and post-stroke individuals. Our results reported a maximum classification accuracy of 98.6% and 74.3% using the proposed method of EEG-TCFNet in subject-dependent strategy and subject-independent strategy, respectively. Overall, FNB usage in all three CNN topologies outperformed those without FNB. In addition, we compared the addition of FNB to other state-of-the-art methods and obtained higher classification accuracies on account of the integration with FNB. The remarkable performance of the proposed model, EEG-TCFNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced P300-based BCIs for smart home interaction within natural settings.

Intelligent Reconfigurable Photovoltaic System

The global maximum power point tracking of a PV array under partial shading represents a global optimization problem. Conventional maximum power point tracking algorithms fail to track the global maximum power point, and global optimization algorithms do not provide global maximum power point in real-time mode due to a slow convergence process. This paper presents an intelligent reconfigurable photovoltaic system on the basis of a modified fuzzy neural net that includes a convolutional block, recurrent networks, and fuzzy units. We tune the modified fuzzy neural net based on modified multi-dimension particle swarm optimization. Based on the processing of the sensors’ signals and the photovoltaic array’s image, the tuned modified fuzzy neural net generates an electrical interconnection matrix of a photovoltaic total-cross-tied array, which reaches the global maximum power point under non-homogeneous insolation. Thus, the intelligent reconfigurable photovoltaic system represents an effective machine learning application in a photovoltaic system. We demonstrate the advantages of the created intelligent reconfigurable photovoltaic system by simulations. The simulation results reveal robustness against photovoltaic system uncertainties and better performance and control speed of the proposed intelligent reconfigurable photovoltaic system under non-homogeneous insolation as compared to a GA-based reconfiguration total-cross-tied photovoltaic system.

Picture fuzzy tolerance graphs with application

In this study, the notions of picture fuzzy tolerance graphs, picture fuzzy interval containment graphs and picture fuzzy phi -tolerance graphs are established. Three special types of picture fuzzy tolerance graphs having bounded representations are introduced and studied corresponding properties of them taking phi as max, min and sum functions. Also, picture fuzzy proper and unit tolerance graphs are established and some related results are investigated. The class of picture fuzzy phi -tolerance chaingraphs which is the picture fuzzy phi -tolerance graphs of a nested family of picture fuzzy intervals are presented. A real-life application in sports competition is modeled using picture fuzzy min-tolerance graph. Also a comparison is given between picture fuzzy tolerance graphs and intuitionistic fuzzy tolerance graphs.

Design an Optimal Fractional Order PID Controller Based on New Algorithms and a Fuzzy Logic Controller to Regulate Type 1 Diabetes Patients

Diabetes is one of the most important diseases that researchers have focused on in scientific research since the time, because of the seriousness of this disease if it is not properly dealt with, especially with the emergence of some global epidemics such as Corona Virus (COVID 19), as the pancreas is the organ responsible for regulating sugar in the blood by secreting the insulin enzyme, insulin is widely used to control blood sugar. Therefore, it is important that the required insulin value is constant and controlled. The aim of this study is to control the blood glucose value that is achieved as a desired value and to maintain it as a constant value using a proportional, integral, and derivative control unit (FOPID) fractional order of the control parameters. In this research, the new control unit is applied to Bergman's mathematical model as a non-linear and simple model that simulates the mechanism of the interaction of glucose and insulin in the blood, and based on this, a closed control loop was designed to regulate the level of blood sugar to be an automatic control of blood glucose using the measured data from Special sensor. The contribution in this scientific paper is to define the (FOPID) parameters according to the closed loop responses of the system, and these parameters were adjusted using new meta-heuristic algorithms including the Invasive Weed Optimization (IWO), the PSO Particle Swarm optimization, the Genetic Algorithm (GA), The bat optimization algorithm (BA) and (ACO). As a result, the results of the five modern algorithms were compared based on several criteria to find out which one was better using MATLAB / SIMULINK simulation. It was found that the IWO algorithm performs better than PSO. The simulation results of the closed-loop system of this controller at the time of settling, overshoot and control inputs indicate very positive results compared to previous results. In addition, a new method has been proposed which is to design a pump in the form of a valve to control insulin pumping by controlling it with the fuzzy logic control unit, which in turn, we obtained better results, compared to the results of other previous studies.

Remote sensing image segmentation based on the fuzzy deep convolutional neural network

ABSTRACT Remote sensing image segmentation has large uncertainty related to the heterogeneity of similar objects and complex spectrum in satellite images, causing the traditional segmentation methods to be greatly limited. Existing semantic segmentation methods represented by deep learning have made breakthrough progress. However, traditional deep learning methods, such as deep convolution neural network, are a completely deterministic model, which cannot describe the uncertainty of remote sensing image well. To solve this problem, a new deep neural network combined with fuzzy logic units is proposed in this paper, called RSFCNN (Remote Sensing image segmentation with Fuzzy Convolutional Neural Network). The network integrates convolution units and fuzzy logic units. Convolution units are used to extract discriminant features with different proportions, thus providing comprehensive information for pixel-level remote sensing image segmentation. Fuzzy logic units are used to deal with various uncertainties and provide more reliable segmentation results, and each unit handles the feature maps at a particular image scale by Gaussian blur function. Experiments were carried out on two data sets, and the results showed that RSFCNN has higher segmentation accuracy and better performance than state-of-the-art algorithms.

Unit hydrograph identification based on fuzzy regression analysis

A methodology has been developed to treat the uncertainties of the unit hydrograph rainfall-runoff transformation based on the fuzzy linear regression model. The components of the unit hydrograph are proposed to be symmetric triangular fuzzy numbers. The main idea is to develop a fuzzified version of the widely—used unit hydrograph. The input of the model is the effective rainfall (which is a set of crisp numbers) and the output is the runoff (which is a set of fuzzy numbers). The problem of determining the fuzzy components of the unit hydrograph is transformed into a linear optimization approach based on the fuzzy linear regression of Tanaka. The condition applied is that the data must be included in the fuzzy band of discharge produced by the fuzzy unit hydrograph. However, some modifications of the widely-used fuzzy regression are proposed in order to be compatible with the unit hydrograph implementation. The model was tested based on twenty real rainfall-runoff events, the conclusion being that it works well in the case of an individual storm event. However, the consideration of many storms seems to be required in order to increase the applicability of the produced fuzzy hydrograph to other storms in such a way that the fuzziness is increased. Several measures of suitability are proposed in order to check the efficiency of the proposed method. Several other useful comments according to the comparison with other unit hydrograph considerations are made.

Quantum gravity and Riemannian geometry on the fuzzy sphere

We study the quantum geometry of the fuzzy sphere defined as the angular momentum algebra [x_i,x_j]=2imath lambda _p epsilon _{ijk}x_k modulo setting sum _i x_i^2 to a constant, using a recently introduced 3D rotationally invariant differential structure. Metrics are given by symmetric 3 times 3 matrices g and we show that for each metric there is a unique quantum Levi-Civita connection with constant coefficients, with scalar curvature frac{1}{2}(mathrm{Tr}(g^2)-frac{1}{2}mathrm{Tr}(g)^2)/det (g). As an application, we construct Euclidean quantum gravity on the fuzzy unit sphere. We also calculate the charge 1 monopole for the 3D differential structure.

A Profit Maximisation Solid Transportation Problem Using Genetic Algorithm in Fuzzy Environment

In this paper, a solid transportation problem has been considered in which the transportation is accomplished in two stages – firstly, from the origin(s) to the near by station(s) of the destination(s) and secondly, from the near by station(s) to the exact destination(s). Here, a fuzzy AUD (all unit discount) policy has been introduced based upon the amount of transportation along with a fuzzy fixed charge. In addition, a budget constraint has been incorporated taking fuzzy unit transportation cost. Then the proposed model has been converted into a single objective optimisation problem using interval arithmetic method. To solve the model, Genetic Algorithm (GA) has been used depending on roulette wheel selection, crossover and mutation. Finally, a numerical example has been illustrated to study the feasibility of the model.

Law as a culturally constituted sign-system – A space for interpretation

AbstractThe aim of the paper is to discuss legal language systems as culturally constituted sign-systems that are continuously evolving in time and space. To understand messages formulated in legal languages, one needs to realize that law is culture-bound, which in turn means that law reflects society’s mentality, tolerance, knowledge, social perceptions, etc. At the same time, law is a living reality impacted by various global phenomena and other legal systems. Therefore, this legal reality has “divergent potentialities” (Hasegawa, Ko. 2016. “A glance at the dynamics of ‘confluence’ in a legal system – notes on H. Patrick Glenn’s insights concerningLegal Traditions of the World”. InTransnational Legal Theory. Vol. 7/1: 1–8, 3), which enable it to develop in various directions depending on wider social, political and technological contexts. Additionally, when communicating law interlingually and intralingually, one needs to take into account the knowledge of senders and recipients since the degree of commensurability of law depends on the uniformity of interpretation on meanings. When discussing the issues of sign meaning interpretation, the authors will focus on non-decomposable units and fuzzy units. The meaning of such terms is subject to interpretation through the prism of tacit knowledge. Therefore, the interpretation of any culturally constituted sign-system is burdened with some loss of information and meaning deficiency.

A new fuzzy unit selection cost function optimized by relaxed gradient descent algorithm

In data-driven corpus-based text-to-speech synthesis systems, the main issue is to select the most natural-sounding sequence of acoustic units without unnatural acoustic transitions, and to minimize all acoustic mismatches at the concatenation points. Unit selection algorithms incorporating unit selection cost functions have been known to synthesize speech close to natural quality. However, these algorithms operate over large acoustic inventories with huge number of acoustic units in a broad spectrum of linguistic, prosodic and acoustic contexts, and with a huge number of concatenation possibilities. Moreover, the shape of the unit selection cost function, which evaluates the cost of concatenating two subsequent acoustic units, is modelled manually in a time-consuming and laborious iterative process, which is based on subjective evaluation. Since this process must be repeated for any new acoustic inventory, or even after changes in a given acoustic inventory, we propose instead a new fuzzy unit selection cost function. We further propose to optimize fully automatically the shape of the fuzzy unit selection cost function to the given acoustic inventory’s context by using a relaxed gradient descent algorithm, where the subjective tests are replaced by a novel objective measure needed to evaluate unit selection cost function performance. Furthermore, the proposed approach is fully interpretable and also highlights insights into which parts of the fuzzy unit selection cost function’s shape could be further improved. The experiments show that the optimized fuzzy unit selection cost function significantly outperforms the baseline fuzzy unit selection cost function. Moreover, the results prove that the unit selection optimization algorithm is capable of finding the optimal shape of the fuzzy unit selection cost function, even when optimized over a small subset of sentences.

Determining weight coefficients for an optimal system of control over electric energy generation in a combined electric power system

This paper reports a solution to a relevant scientific task of determining the weight coefficients for an optimal system of automated control over the level of electric energy generation by renewable energy sources in a combined electric power system. It has been established that existing control systems of electric power generation by renewable sources, based on solving a problem on multicriteria optimization, do not take into consideration a change in the quantitative and qualitative composition of electric consumers over time.It has been proposed, in order to determine those weight coefficients that influence the resulting solution to the problem on multicriterial optimization, to employ a mathematical apparatus of fuzzy logic. This has made it possible, in contrast to existing systems, to better account for a time-dependent change in the quantitative and qualitative composition of electricity consumers. It has been shown that it is expedient to choose, as the input parameters for a fuzzy model, the values for a coefficient of consumer load and a coefficient of load importance that takes into consideration the sensitivity of electric consumers to voltage deviations.A database of rules for a fuzzy system of weight coefficients determination has been constructed, which includes 15 rules of fuzzy production. The Mamdani algorithm has been used as an algorithm of fuzzy logic derivation. To determine the input and output linguistic variables we have used triangular and trapezoidal membership functions. The output parameter was defuzzified by the method of determining the center of gravity (centroid method).We have performed computer simulation of the optimal system of an automated system of control over the level of electric energy generation by renewable energy sources using the designed fuzzy unit for determining weight coefficients. Analysis of the simulation results has shown that the devised system of automated control makes it possible to increase the level of electricity generation to a network, as compared to existing systems

Fuzzy logic-based multi-level shunt active power filter for harmonic reduction

In this paper, using a three level diode clamped multilevel inverter and DC capacitor, a shunt active power filter (SAPF) is implemented to mitigate the supply current harmonics and compensate reactive power drawn from nonlinear load. The advantage of using three-level inverter paves way to reduced harmonic distortion and switching losses. Fuzzy logic control and unit sine vector control are proposed in this paper for generating reference current for the SAPF. The advantage of fuzzy control is that it is based on a linguistic description and does not require a mathematical model of the system. The implementation of fuzzy logic control (FLC) algorithm is executed using MATLAB fuzzy logic tool box. The proposed pulse width modulation (PWM) method produces the switching signals to the inverter from the sampled reference phase voltage magnitudes as in the case of conventional space vector PWM (SVPWM). The simulation results illustrate that the proposed three-level SAPF with low harmonic content in supply current and in phase with the line voltage. The simulation results are validated with prototype model for demonstrating the effectiveness of the system.

Adaptive Genetic Fuzzy Decisive Technique Based Moving Target Identification using Multichannel SAR Set-up

In the ongoing synthetic aperture radar (SAR) methodology, precise and efficient identification of moving targets is a prominent task. Fractional FT (FrFT)accumulates the energy of the required chirp signal in order to separate it as noise from the chirp.The proposed SAR Moving Target Identification (MTI) process is based on FrFT being combined with the definitive adaptive genetic or neurofuzzy method. The correlation between the transmitted signal and the received signal's FrFT is determined, optimizing the appropriate signal energy and applying it to the decisive adaptive genetic fuzzy unit, which identifies the object location using the fuzzy linguistic rules adaptively.The simulation is conducted by changing the number of targets and number of iterations and the evaluation is performed based on parameters such as missed target rate, detection time and Mean Square Error (MSE), showing that the proposed Adaptive Genetic Fuzzy decisiveMTIsystem located the object with a minimum missed target rate of 0.12 in 5.02s and MSE of 23377.4