Defuzzification Process Research Articles

The Stratic Defuzzifier for discretised general type-2 fuzzy sets

Stratification is a feature of the type-reduced set of the general type-2 fuzzy set, from which a new technique for general type-2 defuzzification, Stratic Defuzzification, may be derived. Existing defuzzification strategies are summarised. The stratified structure is described, after which the Stratic Defuzzifier is presented and contrasted experimentally for accuracy and efficiency with both the Exhaustive Method of Defuzzification (to benchmark accuracy) and the α-Planes/Karnik–Mendel Iterative Procedure strategy, employing 5, 11, 21, 51 and 101 α-planes. The Stratic Defuzzifier is shown to be much faster than the Exhaustive Defuzzifier. In fact the Stratic Defuzzifier and the α-Planes/Karnik–Mendel Iterative Procedure Method are comparably speedy; the speed of execution correlates with the number of planes participating in the defuzzification process. The accuracy of the Stratic Defuzzifier is shown to be excellent. It is demonstrated to be more accurate than the α-Planes/Karnik–Mendel Iterative Procedure Method in four of six test cases, regardless of the number of α-planes employed. In one test case, it is less accurate than the α-Planes/Karnik–Mendel Iterative Procedure Method, regardless of the number of α-planes employed. In the remaining test case, the α-Planes/Karnik–Mendel Iterative Procedure Method with 11 α-Planes gives the most accurate result, with the Stratic Defuzzifier coming second.

A novel intuitionistic fuzzy soft set entrenched mammogram segmentation under Multigranulation approximation for breast cancer detection in early stages

Automated mammogram image segmentation is one of the most important methods in the domain of medical diagnosis and decision systems. Accurate segmentation of mammogram plays a key role for the detection of any kind of abnormality like lesion tissues, cyst in mammogram images for medical diagnosis. In this study, a novel hybrid soft computing entrenched segmentation method for mammograms is introduced for the detection of breast cancer in early stages. Here, we have designed a novel automatic mammogram segmentation method using intuitionistic fuzzy soft sets (IFSS) and Multigranulation rough set. First, the proposed clustering algorithm accomplishes a soft information structure from the source image using IFSSs via multiple fuzzy membership functions with Yager generating function. The IFSS handles the ambiguity among lesion and non-lesion pixels through the hesitant degree while shaping the membership function. To reduce distant pixels which do not belong to the region of interest (ROI), the lesion tissues in mammogram image is segregated by decision making scheme via a rough approximation of a fuzzy concept under the field of multigranulation space. Later, the proposed scheme utilizes soft-information builder with accuracy and roughness scores on multigranulation approximation space for segregation of normal and abnormal (lesion tissues) pixel from mammograms. The proposed model has generated a threshold image from accuracy and roughness scores via defuzzification process. The proposed segmentation model performs better than the existing methods using evaluation metrics like segmentation accuracy, Jeccards similarity coefficient.

A trust-based fuzzy neural network for smart data fusion in internet of things

Internet of Things (IoT) devices generates a vast amount of data from extensive applications. Maintaining the sensed data with low energy consumption, delay time, and adaptive coverage fraction rate proportionally influences the storage capacity. To maintain a trade-off between above-listed factors, we proposed an Elfes Sugeno Fuzzy and Trust-based Neural Networks (ESF-TNN) approach enables 3-algorithms. First, Elfes Probability Sensing (EPS) Model addresses the coverage fraction of each IoT sensor. Second, Sugeno Fuzzy Processing model regulates the energy consumption by proportionately distributing data to nodes without the defuzzification process. Third, Trust-based Neural Data Storage algorithm enriches an adequate data storage capacity by considering the average classification ratio while processing regenerated data packets to pertain each interaction information via Trust Mechanism. Simulation results show that our proposed method effectively covers the monitored area with 15 Joules of energy consumption and 1-ms delay time along with sufficient storage capacity.

A new methodology for solving bi-criterion fractional stochastic programming

<p style='text-indent:20px;'>Solving a bi-criterion fractional stochastic programming using an existing multi criteria decision making tool demands sufficient efforts and it is time consuming. There are many cases in financial situations that a nonlinear fractional programming, generated as a result of studying fractional stochastic programming, must be solved. Often management is not in needs of an optimal solution for the problem but rather an approximate solution can give him/her a good starting for the decision making or running a new model to find an intermediate or final solution. To this end, this author introduces a new linear approximation technique for solving a fractional stochastic programming (CCP) problem. After introducing the problem, the equivalent deterministic form of the fractional nonlinear programming problem is developed. To solve the problem, a fuzzy goal programming model of the equivalent deterministic form of the fractional stochastic programming is provided and then, the process of defuzzification and linearization of the problem is presented. A sample test problem is solved for presentation purposes. There are some limitations to the proposed approach: (1) solution obtains from this type of modeling is an approximate solution and, (2) preparation of approximation model of the problem may take some times for the beginners.</p>

Sistem Peramalan Cuaca dengan Fuzzy Mamdani (Studi Kasus: BMKG Lasiana)

Cuaca merupakan salah satu bagian dari keseharian manusia. Banyak orang yang menggantungkan hidupnya pada keadaan cuaca untuk melakukan setiap kegiatannya. Oleh karena itu dengan diketahuinya peramalan cuaca akan memberikan pertimbangan kepada masyarakat untuk dapat melakukan berbagai aktifitas kehidupan manusia seperti pada bidang penerbangan, pelayaran, pertanian, industri olahan dan lain sebagainya yang bergantung pada kondisi cuaca. Untuk itu pihak BMKG Indonesia setiap harinya bertugas untuk memberikan informasi prakiraan cuaca berdasarkan data-data meteorologi yang ada dengan menggunakan perhitungan yang rumit. Penelitian ini bertujuan membangun suatu sistem yang akan menjadi alternatif bagi pihak BMKG dalam meramalkan cuaca menggunakan fuzzy berdasarkan empat kriteria pendukung yaitu suhu udara, kelembaban udara, kecepatan angin dan tekanan udara. Dalam melakukan prakiraan cuaca menggunakan fuzzy mamdani terdapat beberapa langkah yaitu menentukan himpunan fuzzy, Aplikasi fungsi implikasi menggunakan fungsi MIN, komposisi aturan menggunakan fungsi MAX, dan yang terakhir proses Defuzzifikasi menggunakan metode MOM. Sistem ini akan menghasilkan hasil prakiraan cuaca berdasarkan data suhu udara, kelembaban udara, kecepatan angin dan tekanan udara yang telah dimasukan pengguna sistem dengan menunjukan tingkat keanggotaan dari hasil prediksi. Berdasarkan pengujian yang telah dilakukan disimpulkan bahwa sistem yang dibangun menggunakan fuzzy mamdani dapat melakukan prakiraan cuaca dengan baik dengan tingkat akurasi sistem 61,062 % menggunakan data cuaca perhari sebanyak 1826 data pada tahun 2013-2017, dengan tingkat akurasi terendah terdapat pada tahun 2015 dengan tingkat akurasi 54,247 % dan akurasi tertinggi pada tahun 2017 sebesar 65,207 %.

SIMILARITY BASED FUZZY INFERIOR RATIO FOR SOLVING MULTICRITERIA DECISION MAKING PROBLEMS

The method of Fuzzy Inferior Ratio (FIR) has been recognized as one of advantageous methods in multi criteria decision-making under fuzzy environment as it considers the element of compromise solution between the positive and negative aspect of the evaluation simultaneously. It is considered as an improvised version of Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for solving decision-making problems. However, the FIR utilizes the distance approach in the evaluation in obtaining the compromise solution. A defuzzification process is carried out to transform the fuzzy values into a crisp form. Hence, loss of information may occur in the computation. In this paper, we proposed a similarity-based FIR in order to overcome the above-mentioned problem. A new compromise solution for the proposed FIR is developed and an improvised procedure of FIR is suggested using the similarity measure approach. A comparative analysis between the distance based and the similarity-based FIR is carried out using a case study of preferred client selection for a loan application. The proposed method is found to be effective in solving decision-making problems as the utilization of similarity measure will sufficiently preserve the data information in the computational process of evaluation.

Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

SummaryThis article proposes a novel mammogram enhancement approach using adaptive intuitionistic fuzzy special set (IFSS) with deep convolutional neural network (called MECNNIFS) for visual interpretation of mammography lesions, lumps, and abnormal cells in low‐dose X‐ray images. The proposed MECNNIFS scheme utilizes the membership grade modification by IFSS on low‐dose X‐ray images (mammography). The suggested model attempts to increase the underexposed and abnormal structural regions such as breast lesions, lumps, and nodules on the mammogram. The proposed algorithm initially separates mammograms using convolutional neural networks (CNNs) into foreground and background areas and then fuzzifies the image by intuitionistic fuzzy set theory. Low‐level features of a mammogram of the adjacent part are integrated with CNN in pixel classification during the separation task stage to improve the performance. Hyperbolic regularization and hesitant score have been applied on fuzzy plane to quantify the uncertainty and fuzziness in spatial domain for the proposed contrast enhancement. Finally, an enhanced mammogram is acquired through the process of defuzzification. The results show better quality and performance for improvement of contrast and visual quality in mammograms compared with other state‐of‐the‐art methods.

A fuzzy appraisal model for affective agents adapted to cultural environments using the pleasure and arousal dimensions

Humans use rather vague and imprecise words to express emotions. Therefore, fuzzy logic allows computational affective models to use emotions in the same way that human beings express them. However, people from different cultures and languages assign different meanings to the same emotion word. Unfortunately, there are still no affective computing models that really take these two factors into consideration. In this paper, we propose a fuzzy model of appraisal for multi-agent systems that is adapted to Spanish-speakers. Our methodology has two steps. First, the agent evaluates an event using a set of fuzzy appraisal rules that returns a fuzzy emotion. Then, a defuzzification process returns the Pleasure and Arousal dimensions of the emotion that will be internally represented as a vector in a two-dimensional space. This vector is used to update the agent’s mood according to the agent’s personality. The agent can express this internal emotional state using a fuzzification process that translates the agent’s mood into a fuzzy emotion. This fuzzification process uses the results of an experiment to generate a fuzzy emotion that is adapted to the cultural environment in which the agent is located. This methodology can be easily adapted to other languages.

DESIGNING INFORMATION SUPPORT FOR EVALUATING THE QUALITY OF EMBEDDED SOFTWARE

This article presents a system for evaluating the quality of embedded software using a decision system based on fuzzy logic. These approaches will improve the assessment of software quality, due to its features. This article defines the main criteria for software quality used in assessing the quality of the software. The main literature was examined, in which fuzzy logic was described, decision-making systems using fuzzy logic, as well as software quality assessment systems, including software for embedded systems. The main characteristics and properties of embedded syst ems were considered. Based on the considered characteristics and properties of embedded systems, the ranking of criteria was made, which will be further used in the software quality assessment methodology. The main criteria that are used to evaluate the quality of software were considered, and the criteria presented were distributed according to the degree of influence on the assessment of the quality of software of embedded systems. Fuzzy logic was considered, and more precisely: the basic properties of fuzzy logic and fuzzy numbers, the basic mathematical operators applied to fuzzy numbers. The system for constructing rules for the rule base, as well as the defuzzification process, built on the basis of the centroid method, is analyzed. An example of software evaluation for embedded systems was considered. In this example, linguistic variables were determined, as well as their numerical ranges, which were used for the initial assessment of the quality criteria of this software. Each range of ratings was distributed according to the influence of a criterion on software quality. The output linguistic variable and its numerical value were also determined. In the end, based on the set values, an estimate of the set software was derived. The theoretical result obtained in this article is the basis for constructing a system for evaluating software quality for embedded systems.

Evolving fuzzy neural hydrocarbon networks: A model based on organic compounds

This paper presents a new evolving intelligent model capable of combining the techniques and concepts of artificial neural networks, fuzzy systems and artificial hydrocarbon networks, in which the latter aggregates concepts of organic chemistry to carry out the training of intelligent models. The proposed model has three layers where the first two form a fuzzy inference system and the third layer is responsible for the defuzzification process through concepts based on the bond between carbons and hydrogens. The fuzzification process of the model is based on the techniques of an autonomous data partitioning algorithm that can elicit the number and centers of the clouds that make up the fuzzy neurons in the first layer of the model. Thereby, an evolving algorithm is employed, which uses the data set as a stream in a single-pass incremental mode (allowing fast processing). This is achieved in an unsupervised manner, and thus, to eliminate possible overfitting problems in the subsequent supervised training process, a Bayesian pruning technique is used to identify the neurons that are finally most relevant to the actual supervised approximation and/or classification problem. To validate the proposed approach, binary pattern classification tests, multi-class classification problems and regression problems were performed. The results obtained and compared with other intelligent models in the literature prove that the approach becomes a model capable of extracting knowledge from data sets and using concepts of organic chemistry to perform learning tasks with high degree of reliability.

Optimizing The Fertility Rate of Sugarcane Crops at Precision Agriculture Using The Fuzzy Logic Method

Soil fertility has a significant role in the sugarcane plantation industry to maintain plant fertility so that optimal yield productivity is obtained. The management system that has been used by farmers only based on practices and estimation, so that it can not determine the exact needs of water, lime and fertilizers in each area of the plant. Therefore, we need a system that are able to provide a reference for giving water volume, lime content and fertilization according to the level of nutritional needs of sugarcane plants. This study aims to design a system that is able to provide recommendations for sugarcane needs, based on soil nutrient content using the fuzzy logic method. The first step in this method is the fuzzification process carried out on four types of data used as input parameters, namely soil moisture, soil pH, plant phase, and nutrient content. The next step is choosing the relevant criteria from each assessment to get best alternative. The next stage, a membership function is created to estimate the next process and defuzzification process. According to the result of the study found the value of cost efficiency, optimization of growth in stem height and plant tillers. The resulting cost efficiency is 30.05% compared to the factory method. While the level of optimization of plant growth compared to the factory method, tillering growth increased 8% but the growth of primary stem height was higher by the factory method of 3%.

A new method of time series forecasting using intuitionistic fuzzy set based on average-length

ABSTRACT The major problem in the field of fuzzy time series (FTS) is the accuracy rate in the forecasted values. To overcome this problem here, we propose a model for intuitionistic FTS forecasting based on average-length of interval, which enhances the forecasting result. The proposed model is focused on how to fuzzify the historical time series data. Here, the fuzzification of each observation is intuitionistic fuzzification, which is based on the maximum degree of score function and also establishes intuitionistic fuzzy logical relationships (IFLR) among all intuitionistic fuzzified data set. Here, we use simple arithmetic computations in defuzzification process with measuring the frequency of IFLR. An illustrative example of enrollments at the University of Alabama is used to verify the effectiveness of the proposed model and comparison in terms of RMSE and AFE with some of the existing forecasting models to show its superiority.

Integrated fabric procurement and multi-site apparel production planning with cross-docking: A hybrid fuzzy-robust stochastic programming approach

In this paper, we study the integrated inbound logistics decisions and multi-site aggregate production planning (APP) over the tactical planning horizon in a textile industry. In this regard, supplier selection, order allocation, inbound transportation logistics, and multi-site APP problems are addressed in a multi-period, multi-product and multiple transportation modes under uncertainty. To make procurement and production decisions simultaneously, a mixed-integer nonlinear mathematical programming model is proposed in a hybrid fuzzy-stochastic environment. To solve the proposed model, an efficient multi-stage algorithm is developed by re-formulating with a linearization scheme and employing a novel defuzzification process to cope with the proposed possibilistic programming. Then, a robust two-stage stochastic programming method is applied. Finally, the application of the model and the effectiveness of the solution method are examined through comprehensive numerical studies for the apparel industry.

Optimal Fuzzy Logic Control for Temperature Control Based on Social Spider Optimization

Fuzzy Logic Controller (FLC) is useful for many applications like industrial, home, military and medical applications, so one of main application is temperature control of A proposed controller combines the advantages of controller structure and fuzzy inference for air heater temperature control applications. The input variables of the PDFLC-SSO are error and change of error, while its output is the control signal. PDFLC-SSO has seven membership functions. These functions are triangular shaped. Centroid method (CM) is selected for the defuzzification process. The universe of discourse range is used from (-1 to 1) for each input and output. Temperature heater model was utilized to test the controller; the gain factors was tuned using trial and error method and SSO algorithm to reach the optimum response with minimum settling time, minimum peak overshoot and minimum Integral Time Absolute Error (ITAE). Carried out simulations showed an average improvement in measurements using ITAE. ITAE is 7.46% for a constant set-value using PDFLC-SSO, while it is 9.55 for a constant set-value using PDFLC.

Modeling of Characteristics on Artificial Intelligence IQ Test: a Fuzzy Cognitive Map-Based Dynamic Scenario Analysis

This research article uses a Fuzzy Cognitive Map (FCM) approach to improve an earlier proposed IQ test characteristics of Artificial Intelligence (AI) systems. The defuzzification process makes use of fuzzy logic and the triangular membership function along with linguistic term analyses. Each edge of the proposed FCM is assigned to a positive or negative influence type associated with a quantitative weight. All the weights are based on the defuzzified value in the defuzzification results. This research also leverages a dynamic scenario analysis to investigate the interrelationships between driver concepts and other concepts. Worst and best-case scenarios have been conducted on the correlation among concepts. We also use an inference simulation to examine the concepts importance order and the FCM convergence status. The analysis results not only examine the FCM complexity, but also draws insightful conclusions.

Prediction of Rainfall Characteristics Based on Fuzzy Expert System

Rainfall prediction is one of the most extremely important and trickiest job in the modern world because, Rain is the lifeblood of human survival, of life on our planet. Gorgeous beauty of fuzzy logic was described by various author for prediction in various field. In this work, fuzzy logic is applied in proposing a model to predict rainfall percentage with parameters. Fuzzy Expert System inputs(parameter) include the temperature, humidity, wind speed , Dew point etc., with Output as rainfall Percentage .Here we develop a new model as RPFES Model to predict rainfall percentage for particular geographical location of Tamil Nadu. There are four steps for developing RPFES Model : The first step is Fuzzification process by triangular membership function for representing the input variables and output variables. The next step is Fuzzy Inference with Fuzzy Rules the method applied in this research work are Root Sum Square (RSS). The Root Sum Square of drawing inference was employed to infer the data from the fuzzy rules developed and finally we move to Defuzzification process for getting Rainfall percentage by individually. From RPFES Model, Individual Person get the Rainfall Prediction as Percentage with purely by Fuzzy Logic and not by Metrological Center. Weather Prediction is the major essential and challenging operational responsibilities which are carried out by meteorological services all over the world.

Strategic Mapping of Youth Unemployment With Interval-Valued Intuitionistic Hesitant Fuzzy DEMATEL Based on 2-Tuple Linguistic Values

This study aims to identify the factors that affect youth unemployment in emerging countries. For this purpose, 3 dimensions and 12 criteria are selected as a result of literature review. The analysis process has 3 different steps. Firstly, interval-valued intuitionistic fuzzy sets are created with the help of 2-tuple linguistic data. Additionally, relation matrix is generated by considering these fuzzy sets. In the second process, defuzzification process is occurred. Finally, the dimensions and criteria are weighted with Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach by using defuzzified data sets. The findings indicate that economic and social inequalities play the most significant role for youth unemployment in emerging countries. On the other side, it is also identified that economic crisis and insufficient education conditions are also important issues which lead to youth unemployment in these countries. Hence, it is recommended that governments should implement fair tax management practices in these countries to minimize economic and social inequalities. Furthermore, education conditions should be improved in the countries. In this framework, an effective education plan can be designed by cooperating with companies in the industry. Thus, labor needs in industry can be identified and education system can be designed according to the needs in the market. With the help of these implementations, it can be much easier for young people to find a job.

Measurement of activeness lecturers in SmartLecturer based learning using fuzzy Mamdani and Sugeno

The success of learning in a classroom that uses supporting media demands lecturer activity. The level of activity of lecturers in class and using learning media such as Smart lecturer can be measured using a fuzzy logic approach. This study aims to measure the level of active lecturers using the approach, namely: Mamdani and Sugeno method. Stages of lecturer activity measurement by forming a fuzzy set, composition rules as many as 24 rules and the defuzzification process using the centroid method that produces the level of activity of each is low, medium and high. Based on 173 lecturer activity data, the results of the Mamdani method indicates low 66%, medium 31%, and high 3%. While the Sugeno method produces a level of activity low 75%, moderate 17%, and high 8%. Therefore, Mamdani method is more suitable for the calculation because the spread of results is relatively evenly distributed at each level.

Implementation of Fuzzy Logic Controller with Different Membership Function Curves

Generally, the applications of power electronic are widely applied in the industrial, household, renewable energy fields, etc. Fuzzy Logic Controller is considered as an effective support control in the application because this implementation is very useful. Fuzzy Logic Controller is more reliable when compared to conventional control systems. This paper provides a Fuzzy Logic Controller based control method to identify the transformation of the membership function curve by using the dsPIC30F4012 microcontroller. In the implementation, the different membership function curves are adopted to obtain the maximum power output. The fuzzification process is needed to convert numerical numbers into fuzzy numbers. The other process is also required to convert the result of fuzzification process, it is called defuzzification process. To obtain the control system performance, the rule is defined in the rule base matrix. The experimental results were done to verify the analysis of Fuzzy Logic Controller.

Efficient Technique to Detect Edge in Images with Fuzzy Rules

There exists an increasing demand to detect edge of an image for many real time applications. In this paper an innovative technique is proposed for detecting text using fuzy rules. The projected system primarily divides the image into fragment of 3 x 3 matrix. The proposed system uses fuzzy rules using input size of eight pixels and one output pixel. The output pixels will either be one among black, white or edge pixel. The fuzzy sytem is applied with sixteen rules for categorizing the pixel as target pixel. Fuzzification is performed which converts the input pixel into the fuzzy interval between zero and one. It is followed by calculating a degree of Hesitation, which is also called as the intuitionstic fuzzy indicator. The last step is the Defuzzification process where the pixel identified as the pixel is converted to its original image pixel with the interval between 1 and 255. The proposed system is weighed against existing edge detecting methods like Canny, Sobel, and ACO algorithm. The proposed algorithm works fine even for exigent scenarios of the image.