سیستم های فازی و کاربردها
Year:2021 | Volume:4 | Issue:1|Papers Count:12

The design of the distribution network of all livestock units is similar to that provided by the management. The woman was taken to safety. The design of the meter is designed to be evenly distributed Different types of snails, especially those suffering from malnutrition, are taken from this It is clear from the method that this method can be used in such a way that it can not be seen in any way. He was arrested. In this research, a group of people will be invited to study and study. The goal is to maximize the total cost, environmental impact and competitiveness of the market. Social justice can be achieved by limiting the production of goods in the region. The customer is under consideration.

Prediction models and classification algorithms are widely used in many science and technology. Among their various methods, Well-known data-driven methods such as neural networks and neuro-fuzzy models because of their characteristics have been considered by many researchers. To develop and overcome the weak points of these models, the concepts of the human brain biological systems are used. Therefore, the brain's emotional limbic system is used to develop these models. Brain Emotional Learning (BEL) is an emotional artificial neural network based on the interaction of the thalamus, cortex, amygdala, and orbitofrontal components. This learning machine has different architectures and learning algorithms. In this paper, the online fuzzy extreme learning machine is used as the amygdala and orbitofrontal component in the brain emotional learning machine. To interact between the main components of the brain emotional learning machine, online recurrent memory sequential fuzzy extreme learning machine with different memory depth and transfer learning ability is used. The final design machine is called Brain Emotional Learning based on Online Recurrent Memory Sequential Fuzzy Extreme Learning Machine (BEL-ORMS-FELM). The proposed cognitive machine is designed based on learning the training data one-by-one but also chunk-by-chunk (with fi, xed or varying length) and it can discard training data that has already been trained. Performance comparison of the proposed method is done with other similar learning methods on the benchmark problems of chaotic time series. The results of analysis and simulations show that the performance and accuracy of the proposed method are higher than other methods.

Reconfiguration and installation of distributed generation sources are some of the methods used to reduce losses, improve voltage stability and increase reliability in power distribution networks. Finding the switches that should definitely participate in the reconfiguration program and determining their status at each stage of the reconfiguration is one of the most important goals in the optimal operation of the network. This paper examines the optimal placement of distribution network switches and distributed generation sources in order to improve reliability, reduce losses and improve voltage stability and thus increase network load. In this paper, in order to improve the voltage stability, in comparison with the maximum load index known as (λ, max), another index called singular values of Jacobin matrix is introduced and the efficiency of the two is compared. Also, in order to reduce the heavy reliability calculations observed in the Monte Carlo method, the minimum cut set method and the probabilistic model have been used to model the elements of the distribution system at load points. Distributed generation sources with random and variable nature and system loads hourly and with the triple nature of residential, commercial and industrial are considered. Due to the multiple objective functions, the NSGA2 multi-objective optimization algorithm is used to optimize the objective functions and the fuzzy function membership method is used to determine the optimal answer. The simulation results are performed on the 33-bus IEEE distribution network and the efficiency, accuracy and possible weaknesses of the proposed method are shown.

One of the most important developments in fuzzy theory is hesitant fuzzy sets. In this expansion, in addition to the possibility of considering numbers fuzzy, the opinions of different decision-makers can be considered to prevent inconsistencies and conflicts between their views and, of course, to make the data more consistent with the prevailing reality of the issues. In this research, we intend to carefully present a new method with comparative fuzzy sets to compare these types of numbers with the characteristics that hesitant fuzzy sets have. For this purpose, while paying attention to the fuzzy nature of the opinion of each of the different experts, the commonality between their views and issues such as optimistic or pessimistic attitudes is sufficiently careful. The issue of determining the tree with minimum weight is one of the main and widely used issues in various branches of science and engineering. Given the widespread use of this problem in streaming networks and the discussion of uncertainties in real-world application problems, the following paper presents the efficiency process for finding the minimum peripheral tree with hesitant fuzzy data, in which a new ranking method is proposed. Used in this post. We then solve a numerical example to verify process performance. At the end, the conclusion of the research and suggestions for further research are given.

In recent decades, the issue of energy demand and the factors affecting it has become a very controversial. A Survey of energy consumption in Iranian agricultural businesses shows that in recent years, along with increasing production and increasing the mechanization coefficient, the consumption of various energy carriers, including electricity, has increased. In several studies the factors affecting electricity consumption in the agricultural sector have been studied, using econometric methods. However, due to the limitations of classical regression methods, in this study the fuzzy method has been used to investigate the effect of export diversity index and financial development on electricity consumption of agricultural businesses during the period 1970-2019. The application of fuzzy regression has been due to the flexibility of this model and the lack of assumptions limiting classical regression methods. The results show that the variable of agricultural production has the greatest impact on electricity consumption in this sector. Also, the variables of export diversity index and financial development in the upper and lower bounds had a positive and significant effect on increasing electricity consumption in the agricultural sector. Therefore, identifying the variables affecting agricultural electricity demand and examining the extent of their impact on electricity consumption, it can be used to make appropriate decisions and policies to optimize energy consumption with the aim of economic development and production growth of agricultural businesses.

In this paper, the aim is to find the nearest trapezoidal fuzzy number to a given fuzzy number which preserves the convex combination of support and core intervals of that fuzzy number. This allows the decision maker to select the preferred approximation of a fuzzy number from a class of trapezoidal approximations. Nowadays, the fuzzy concepts are widely used in many real-world engineering applications, such as population models, control chaotic systems, economics and finance, artificial intelligence, computer science, expert systems, management science, operations research, pattern recognition, robotics and others. Because of the existence of fuzzy parameters, computational complexity is the cost of fuzzy system and this matter has captured the attention of researchers for introducing methods and decreasing this cost. In general, most of the fuzzy-based algorithms use a defuzzification process that maps a fuzzy parameter into a crisp one. Obviously, in most cases, too much important information is lost by converting fuzzy sets into a set of real numbers. It seems that we should accept some criteria and apply a framework for constructing a defuzzyfication process. Van Leekwijck et al. in [21] presented a set of criteria for defuzzification strategies and classified the most widely used defuzzification techniques into different groups and they examined the prototypes of each group with respect to the defuzzification criteria.

In this paper, the design steps of a multi-model adaptive receding horizon controller for a nonlinear dynamic system are investigated. To implement this control structure, the Takagi-Sugno-Kong (TSK) fuzzy inference system (TSK) has been used to predict the behavior of the dynamic system on a receding horizon. In the proposed controller, the linear part of the TSK fuzzy model is used as a linear model to implement a multi-stage receding horizon controller to calculate the optimal control input sequence. A standard least square algorithm is used to identify the rules consequent parameters of the TSK model. A clustering method is used for partitioning the input-output space in order to generate TSK fuzzy model. Each cluster represents a functional area of the complex dynamic system in the input-output space. In the proposed control strategy, it is assumed that the variables which are used in the premise of the rules are also those which are used in linear models that describe the consequents of the rules. For proper control of the nonlinear system, multiple models are used on the receding horizon. In order to evaluate the proposed control strategy, the proposed control structure has been used to control the power of a nuclear reactor in the charge pursuit problem. The simulation results show the good performance of the proposed control structure.

The present study aims to evaluate and improve the performance of human resources with an integrated approach of fuzzy hierarchical analysis and TOPSIS in the model of organizational excellence in Shahid Bahonar Copper Industries in Kerman. In order to prioritize each of the criteria of the organizational excellence model in order to improve the performance of human resources from the perspective of experts and employees, the fuzzy hierarchical analysis technique was used. First place, leadership criteria with the final score in the second place, business results (process) with the final score in the third place. In this study, in order to prioritize the sub-criteria of each of the research criteria, the TOPSIS model was used. External stakeholders are ranked first and below the achievement criteria of leadership improvement projects are ranked last. According to the results of prioritizing the obtained criteria, it was found that in order to achieve the desired performance of human resources using organizational excellence, the company needs to manage the correct strategy and principles in order to effectively implement the processes governing the company.

Nowadays, feature selection is an essential step in machine learning due to the increase in high-dimensional data. In addition, with the continuous production of various data and the high dimensions of these data, practical methods in reducing the dimensions, mainly feature selection, are needed. Many data can be grouped into multi-label data. This means that each instance in a data set can belong to more than one class label. This paper proposes a feature selection method based on hesitant fuzzy sets to reduce the dimensions of multi-label data. In this method, we have used a combination of three different criteria in measuring the correlation between features and labels, as well as three similarity criteria to measure the similarity between features. We have considered these methods as the experts in feature evaluation. Correlation and similarity combinations have been performed based on the concept of information energy in hesitant fuzzy sets. To demonstrate the effectiveness of the proposed method, comparisons have been made with new methods in the field of multi-label feature selection. These comparisons are based on the classification accuracy, Hemming loss, and execution time of the algorithm.

A fuzzy logic based bi-objective path planning algorithm for multiple mobile robots in unknown dynamic environment.

Today, various models with different estimation methods are introduced and used in data modeling. The appropriateness of each method of estimating statistical models in the fit of a dataset is based on a specific goodness-of-fit criterion (or a specific objective function). Also, the goodness-of-fit index of any statistical model (including classical or fuzzy regression models) is defined and formulated according to the structure of that model. Therefore, using and applying only one criterion to compare the goodness-of-fit of a diverse set of statistical models leads to oblique/biased and directional decisions. In fact, such a process leads to prioritization of the models that their objective functions are the same as the evaluation criterion and/or their objective functions are structurally proportional to the evaluation criteria. Therefore, considering only one-criterion to evaluate the goodness-of-fit of the models deprives them of the possibility of a fair and equitable comparison, which is very challenging. Our main goal in this paper is to provide and propose an appropriate framework in the context of multi-criteria decision making to overcome the challenge. In this method, it is possible to aggregate a wide range of evaluation criteria from different point of views to generate a generalized evaluation criterion in order to identify the optimal model. Finally, the proposed approach is employed to rank the fit of 22 different fuzzy regression models.

In statistical quality control, there are two important issues including control cards and performance indicators. Control cards are used to check the stability of the process and after determining the stability, the main parameters of the process are estimated based on the available data. Process performance indicators are numerical criteria that measure the degree of product compliance Production represents a process with specifications considered by customers or manufacturers they give. If the quality of products depends on only one variable, one-variable indices are used, and in processes where the quality is related to two or more dependent variables, multivariate indices are used. In the real world, many measurements are inaccurate because of the ambiguity. Therefore, fuzzy logic is used to describe them. Here are the cases where the specifications of the variable / variables under study are fuzzy. Therefore, fuzzy indicators should be used to measure process efficiency. In this paper, for univariate processes, we introduce two univariate fuzzy efficiency indices. In addition, for multivariate processes, we provide a fuzzy multivariate efficiency index and a fuzzy multivariate efficiency vector. We use practical examples to show how to use the proposed indicators.