Статті в журналах з теми "Fuzzy neural networks Mamdani and Tsukamoto"
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Meng, Lei, Shoulin Yin, and Xinyuan Hu. "An improved Mamdani Fuzzy Neural Networks Based on PSO Algorithm and New Parameter Optimization." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 1 (January 1, 2016): 201. http://dx.doi.org/10.11591/ijeecs.v1.i1.pp201-206.
Повний текст джерелаАнотація:
As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.
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Lucchese, Luísa Vieira, Guilherme Garcia de Oliveira, and Olavo Correa Pedrollo. "Mamdani fuzzy inference systems and artificial neural networks for landslide susceptibility mapping." Natural Hazards 106, no. 3 (February 8, 2021): 2381–405. http://dx.doi.org/10.1007/s11069-021-04547-6.
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Mohammed, Hind R., and Zahir M. Hussain. "Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images." Computation 9, no. 3 (March 17, 2021): 35. http://dx.doi.org/10.3390/computation9030035.
Повний текст джерелаАнотація:
Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.
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Akhter, Tanjima, Md Ariful Islam, and Saiful Islam. "Artificial Neural Network based COVID-19 Suspected Area Identification." Journal of Engineering Advancements 01, no. 04 (December 2020): 188–94. http://dx.doi.org/10.38032/jea.2020.04.010.
Повний текст джерелаАнотація:
This paper deals with the symptoms based COVID-19 suspected area identification using an artificial neural network by which a country or region can be divided into red, yellow, and green zone representing the highly infected area, moderate infected area, and controlled or low infected area, respectively. At first, an online survey of twenty (20) patients was conducted based on the nine (09) major symptoms of COVID-19. Then, a model based on the fuzzy logic system was designed consisting of COVID-19 symptoms identification, fuzzification, rule evaluation, fuzzy inference mechanism, etc. for getting the data sets to be trained in neural networks. For different combinations of 09 symptoms, different rules were generated and evaluated for possible recommendations. Based on different rules, three possible outputs representing high infection probability, medium infection probability, and low infection probability were obtained using the Mamdani inference mechanism. These outputs were termed as red, yellow, and green zone separated by the crisp value of +1, 0, -1, respectively, and considered as target data to be trained in neural networks.
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THEODORIDIS, DIMITRIOS, YIANNIS BOUTALIS, and MANOLIS CHRISTODOULOU. "DYNAMICAL RECURRENT NEURO-FUZZY IDENTIFICATION SCHEMES EMPLOYING SWITCHING PARAMETER HOPPING." International Journal of Neural Systems 22, no. 02 (April 2012): 1250004. http://dx.doi.org/10.1142/s0129065712500049.
Повний текст джерелаАнотація:
In this paper we analyze the identification problem which consists of choosing an appropriate identification model and adjusting its parameters according to some adaptive law, such that the response of the model to an input signal (or a class of input signals), approximates the response of the real system for the same input. For identification models we use fuzzy-recurrent high order neural networks. High order networks are expansions of the first-order Hopfield and Cohen-Grossberg models that allow higher order interactions between neurons. The underlying fuzzy model is of Mamdani type assuming a standard defuzzification procedure such as the weighted average. Learning laws are proposed which ensure that the identification error converges to zero exponentially fast or to a residual set when a modeling error is applied. There are two core ideas in the proposed method: (1) Several high order neural networks are specialized to work around fuzzy centers, separating in this way the system into neuro-fuzzy subsystems, and (2) the use of a novel method called switching parameter hopping against the commonly used projection in order to restrict the weights and avoid drifting to infinity.
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Grace, Asogbon Mojisola, and Samuel Oluwarotimi Williams. "Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation." International Journal of Intelligent Information Technologies 12, no. 1 (January 2016): 47–62. http://dx.doi.org/10.4018/ijiit.2016010103.
Повний текст джерелаАнотація:
Credit risk evaluation techniques that aid effective decisions in credit lending are of great importance to the financial and banking industries. Such techniques assist credit managers to minimize the risks often associated with wrong decision making. Several techniques have been developed in the time past for credit risk evaluation and these techniques suffer from one form of limitation or the other. Recently, powerful soft computing tools have been proposed for problem solving among which are the neural networks and fuzzy logic. In this study, a neural network based on backpropagation learning algorithm and a fuzzy inference system based on Mamdani model were developed to evaluate the risk level of credit applicants. A comparative analysis of the performances of both systems was carried out and experimental results show that neural network with an overall prediction accuracy of 96.89% performed better than the fuzzy logic method with 94.44%. Finding from this study could provide useful information on how to improve the performance of existing credit risk evaluation systems.
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Jacquin, Alexandra P., and Asaad Y. Shamseldin. "Review of the application of fuzzy inference systems in river flow forecasting." Journal of Hydroinformatics 11, no. 3-4 (July 1, 2009): 202–10. http://dx.doi.org/10.2166/hydro.2009.038.
Повний текст джерелаАнотація:
This paper provides a general overview about the use of fuzzy inference systems in the important field of river flow forecasting. It discusses the overall operation of the main two types of fuzzy inference systems, namely Mamdani and Takagi–Sugeno–Kang fuzzy inference systems, and the critical issues related to their application. A literature review of existing studies dealing with the use of fuzzy inference systems in river flow forecasting models is presented, followed by some recommendations for future research areas. This review shows that fuzzy inference systems can be used as effective tools for river flow forecasting, even though their application is rather limited in comparison to the popularity of neural networks models. In addition to this, it was found that there are several unresolved issues requiring further attention before more clear guidelines for the application of fuzzy inference systems can be given.
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Mlakić, Dragan, Srete N. Nikolovski, and Goran Knežević. "An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (June 1, 2016): 1294. http://dx.doi.org/10.11591/ijece.v6i3.10147.
Повний текст джерелаАнотація:
The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.
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Mlakić, Dragan, Srete N. Nikolovski, and Goran Knežević. "An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (June 1, 2016): 1294. http://dx.doi.org/10.11591/ijece.v6i3.pp1294-1304.
Повний текст джерелаАнотація:
The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.
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KUMAR, ASHWANI, D. P. AGRAWAL, and S. D. JOSHI. "MULTISCALE NEUROFUZZY MODELS FOR FORECASTING IN TIME SERIES DATABASES." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 06 (November 2007): 859–78. http://dx.doi.org/10.1142/s0219691307002087.
Повний текст джерелаАнотація:
Multiscale neurofuzzy modeling combines the multiresolution property of the wavelet transform with the regression ability of neurofuzzy systems. A wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in detail and approximation. A neurofuzzy system is then trained on each of the relevant resolution scales (i.e. those scales where significant events are detected); and individual wavelet forecasts are recombined to form the overall forecast. The neurofuzzy models developed in this paper are based on Mamdani and Takagi–Sugeno–Kang approaches to the problem of fuzzy modeling based on the strategy knowledge expressed by the input-output data. Within these approaches, the proposed Neural-Fuzzy Inference System (NFIS) provides several methods that represent different alternatives in the fuzzy modeling process and how they can be integrated with the learning power of neural networks. Simulation results carried out on a forecasting problem associated with stock market, are included to demonstrate the potential of the proposed forecasting scheme.
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Evgenev, G. B. "Synergetic knowledge bases." Ontology of designing 11, no. 1 (March 30, 2021): 76–88. http://dx.doi.org/10.18287/2223-9537-2021-11-1-76-88.
Повний текст джерелаАнотація:
Modern knowledge bases must largely correspond to human thinking and the reality of the world. Synergetic knowledge bases create the possibility of joint use of both "hard" computing, which require the accuracy and unique-ness of the solution, and "soft" computing, allowing a given error and uncertainty for a specific problem. A methodolo-gy for creating synergetic systems for the representation of knowledge using artificial intelligence technologies is pro-posed. The methodology is based on knowledge base methods and can be used to develop design and management systems in industries. A model for representing linguistic variables is proposed. The method of creating fuzzy knowledge bases and the stages of the inference mechanism are considered. The fuzzy inference is described using the example of the Mamdani mechanism. A functional diagram of the creation of fuzzy inference systems based on a structured clear knowledge module is proposed. A method for creating knowledge bases for the implementation of neural network models is considered. An example of a knowledge base for training neural networks is given.
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Siddiqui, Shahan Yamin, Atifa Athar, Muhammad Adnan Khan, Sagheer Abbas, Yousaf Saeed, Muhammad Farrukh Khan, and Muhammad Hussain. "Modelling, Simulation and Optimization of Diagnosis Cardiovascular Disease Using Computational Intelligence Approaches." Journal of Medical Imaging and Health Informatics 10, no. 5 (May 1, 2020): 1005–22. http://dx.doi.org/10.1166/jmihi.2020.2996.
Повний текст джерелаАнотація:
Background: To provide ease to diagnose that serious sickness multi-technique model is proposed. Data Analytics and Machine intelligence are involved in the detection of various diseases for human health care. The computer is used as a tool by experts in the medical field, and the computer-based mechanism is used to diagnose different diseases in patients with high Precision. Due to revolutionary measures employed in Artificial Neural Networks (ANNs) within the research domain in the medical area, which appear to be in the data-driven applications usually described in the domain of health care. Cardio sickness according to name is a type of an ailment that is directly connected to the human heart and blood circulation setup, so it should be diagnosed on time because the delay of diagnosing of that disease may lead the sufferer to death. The research is mainly aimed to design a system that will be able to detect cardiovascular sickness in the sufferer using machine learning approaches. Objective: The main objective of the research is to gather information of the six parameters that is age, chest pain, electrocardiogram, systolic blood pressure, fasting blood sugar and serum cholesterol are used by Mamdani fuzzy expert to detect cardiovascular sickness. To propose a type of device which will be successfully used in overcoming the cardiovascular diseases. This proposed model Diagnosis Cardiovascular Disease using Mamdani Fuzzy Inference System (DCD-MFIS) shows 87.05 percent Precision. To delineate an effective Neural Network Model to predict with greater precision, whether a person is suffering from cardiovascular disease or not. As the ANN is composed of various algorithms, some will be handed down for the training of the network. The main target of the research is to make the use of three techniques, which include fuzzy logic, neural network, and deep machine learning. The research will employ the three techniques along with the previous comparisons, and given that, the results will be compared respectively. Methods: Artificial neural network and deep machine learning techniques are applied to detect cardiovascular sickness. Both techniques are applied using 13 parameters age, gender, chest pain, systolic blood pressure, serum cholesterol, fasting blood sugar, electrocardiogram, exercise including angina, heart rate, old peak, number of vessels, affected person and slope. In this research, the ANN-based research is one of the algorithms collections, which is the detection of cardiovascular diseases, is proposed. ANN constitutes of many algorithms, some of the algorithms are employed in the paper for the training of the network used, to achieve the prediction ratio and in contrast of the comparison of the mutual results shown. Results: To make better analysis and consideration of the three frameworks, which include fuzzy logic, ANN, Deep Extreme Machine Learning. The proposed automated model Diagnosis Cardiovascular Disease includes Fuzzy logic using Mamdani Fuzzy Inference System (DCD-MFIS), Artificial Neural Network (DCD–ANN) and Deep Extreme Machine Learning (DCD–DEML) approach using back propagation system. These frameworks help in attaining greater precision and accuracy. Proposed DCD Deep Extreme Machine Learning attains more accuracy with previously proposed solutions that are 92.45%. Conclusion: From the previous comparisons, the propose automated Diagnosis of Cardiovascular Disease using Fuzzy logic, Artificial Neural Network, and deep extreme machine learning approaches. The automated systems DCDMFIS, DCD–ANN and DCD–DEML, the framework proposed as effective and efficient with 87.05%, 89.4% and 92.45 % success ratios respectively. To verify the performance which lies in the ANNs and computational analysis, many indicators determining the precise performance were calculated. The training of the neural networks is made true using the 10 to 20 neurons layers which denote the hidden layer. DEML reveals and indicates a hidden layer containing 10 neurons, which shows the best result. In the last, we can conclude that after making a consideration among the three techniques fuzzy logic, Artificial Neural Network and Proposed DCD Deep Extreme Machine, the Proposed DCD Deep Extreme Machine Learning based solution give more accuracy with previously proposed solutions that are 92.45%.
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Kannamma, R., and K. S. Umadevi. "Neuro-Fuzzy-Based Frame Pre-Emption Using Time-Sensitive Networking for Industrial Ethernet." Journal of Information & Knowledge Management 20, Supp01 (February 2021): 2140008. http://dx.doi.org/10.1142/s0219649221400086.
Повний текст джерелаАнотація:
IEEE802.1 Time-Sensitive Networking (TSN) makes it conceivable to convey the data traffic of time as well as critical applications using Ethernet shared by different applications having diversified Quality of Service (QoS) requirements for both TSN and non-TSN. TSN assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data for time-critical traffic. By holding networking resources for basic traffic, and applying different queuing and traffic shaping strategies, TSN accomplishes zero congestion loss for basic time-critical traffic. In proposed system, backpropagation algorithm is used to train the training set and fuzzy inference system methodologies such as Mamdani fuzzy inference system which has fuzzy inputs and fuzzy outputs, Sugeno FIS which has fuzzy inputs and a crisp output and adaptive-network-based fuzzy inference system has obtained from the neural network and fuzzy logic. The proposed system uses neuro-fuzzy techniques to handle frame pre-emption and reduces the time taken for decision making. It presents a decision making process using the traffic class.
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Li, Weijie. "Consumer Decision-Making Power Based on BP Neural Network and Fuzzy Mathematical Model." Wireless Communications and Mobile Computing 2021 (August 11, 2021): 1–9. http://dx.doi.org/10.1155/2021/6387633.
Повний текст джерелаАнотація:
In real life, because of the uncertainty of risk, incomplete information, perceived cost, and other factors, there are irrational behaviors in the decision-making power of consumers, so it is of great practical significance to study the decision-making power of consumers in the choice of countermeasures and personalized product recommendation. The purpose of this paper is to analyze the decision-making power of consumers based on the BP neural network and fuzzy mathematical model. First, the basic theory of artificial neural network and the concepts of set theory and fuzzy reasoning of fuzzy mathematics are described. Second, the behavior prediction model with the equal emphasis on rationality and irrationality and the integration of artificial neural network and fuzzy mathematics are constructed. The comments of a certain mobile phone are selected as the experimental objects to analyze the decision-making reasoning and prediction of individual consumers in the network and the decision-making reasoning of group consumers in the network, the experimental results show that through Mamdani reasoning, behavioral intention = 5.72 . Through the fuzzy set processing, it is finally determined that the consumer’s purchase intention is close to the VT mode, which is “very inclined.” In the first method, the user’s recognition rate of product C1 is 82%, and in the second method, the user’s recognition rate is 55%. The comparison of the two methods is in line with the expectation. The first method extracts the user’s emotion and evaluation information from the comments, fully considers the personalized needs of consumers, and is closer to the prediction results of the system.
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Alvisi, S., G. Mascellani, M. Franchini, and A. Bárdossy. "Water level forecasting through fuzzy logic and artificial neural network approaches." Hydrology and Earth System Sciences 10, no. 1 (February 8, 2006): 1–17. http://dx.doi.org/10.5194/hess-10-1-2006.
Повний текст джерелаАнотація:
Abstract. In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively. All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of the three models is performed by using the same input and output variables. However, in order to evaluate their capability to deal with different levels of information, two different input sets are considered. The former is characterized by significant spatial and time aggregated rainfall information, while the latter considers rainfall information more distributed in space and time. The analysis is made with great attention to the reliability and accuracy of each model, with reference to the Reno river at Casalecchio di Reno (Bologna, Italy). It is shown that the two models based on the fuzzy logic approaches perform better when the physical phenomena considered are synthesised by both a limited number of variables and IF-THEN logic statements, while the ANN approach increases its performance when more detailed information is used. As regards the reliability aspect, it is shown that the models based on the fuzzy logic approaches may fail unexpectedly to forecast the water levels, in the sense that in the testing phase, some input combinations are not recognised by the rule system and thus no forecasting is performed. This problem does not occur in the ANN approach.
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Alvisi, S., G. Mascellani, M. Franchini, and A. Bárdossy. "Water level forecasting through fuzzy logic and artificial neural network approaches." Hydrology and Earth System Sciences Discussions 2, no. 3 (June 22, 2005): 1107–45. http://dx.doi.org/10.5194/hessd-2-1107-2005.
Повний текст джерелаАнотація:
Abstract. In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively. All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of the three models is performed by using the same input and output variables. However, in order to evaluate their capability to deal with different levels of information, two different input sets are considered. The former is characterized by significant spatial and time aggregated rainfall information, while the latter considers rainfall information more distributed in space and time. The analysis is made with great attention to the reliability and accuracy of each model, with reference to the Reno river at Casalecchio di Reno (Bologna, Italy). It is shown that the two models based on the fuzzy logic approaches perform better when the physical phenomena considered are synthesised by both a limited number of variables and IF-THEN logic statements, while the ANN approach increases its performance when more detailed information is used. As regards the reliability aspect, it is shown that the models based on the fuzzy logic approaches may fail unexpectedly to forecast the water levels, in the sense that in the testing phase, some input combinations are not recognised by the rule system and thus no forecasting is performed. This problem does not occur in the ANN approach.
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Fekri Sari, Mahsa, and Soroush Avakh Darestani. "Fuzzy overall equipment effectiveness and line performance measurement using artificial neural network." Journal of Quality in Maintenance Engineering 25, no. 2 (May 7, 2019): 340–54. http://dx.doi.org/10.1108/jqme-12-2017-0085.
Повний текст джерелаАнотація:
Purpose The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the production process. In the existing method, measuring OEE is based on three main elements consisting availability, performance and quality. The purpose of this paper is to evaluate the recognized metrics of production: OEE and overall line effectiveness (OLE) by using smart systems techniques. Design/methodology/approach In this paper, to improve the calculative methods and productivity with three methods: measuring OEE using Mamdani fuzzy inference systems (FIS), measuring OEE using Sugeno FIS, and measuring OLE using FIS and artificial neural networks (ANNs) are proposed. Findings The proposed methodologies aim to decrease some weaknesses of OEE and OLE methods by exploiting intelligent system techniques, such as FIS and ANNs. In particular, this research will solve the following issues that occur in manual and automatic data gathering. This technique is an effective way of measuring OEE and OLE with regard to different weights of losses as well as difference in the weight of the machines. In addition, it allows the operator’s knowledge to take a part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed. Originality/value In relation to other methodologies, it allows the operator’s knowledge to take part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.
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Guzmán, Juan, Ivette Miramontes, Patricia Melin, and German Prado-Arechiga. "Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification." Axioms 8, no. 1 (January 15, 2019): 8. http://dx.doi.org/10.3390/axioms8010008.
Повний текст джерелаАнотація:
The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of an expert which is represented in the fuzzy rules. Subsequently different architectures were made in type-1 and type-2 fuzzy systems for classification, where the parameters of the membership functions used in the design of each architecture were adjusted, which can be triangular, trapezoidal and Gaussian, as well as how the fuzzy rules are optimized based on the ranges established by an expert. The main contribution of this work is the design of the optimized interval type-2 fuzzy system with triangular membership functions. The final type-2 system has a better classification rate of 99.408% than the type-1 classifier developed previously in “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization” with 98%. In addition, we also obtained a better classification rate than the other architectures proposed in this work.
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Terenchuk, S., R. Pasko, O. Panko, and V. Zaprivoda. "MODELS, METHODS, AND MEANS OF REPRODUCTION OF EXPERT KNOWLEDGE IN INTELLIGENT SUPPORT SYSTEM BUILDING-TECHNICAL EXPERTISE." Scientific Journal of Astana IT University, no. 6 (June 30, 2021): 76–87. http://dx.doi.org/10.37943/aitu.2021.43.51.007.
Повний текст джерелаАнотація:
The paper is devoted to solving such a scientific and practical problem as the creation of computerized infocommunication systems for support building-technical expertise to determine the causes of destruction and deformation of buildings and structures. The analysis of the current state of expert activity within the framework of building-technical expertise is carried out. Perspective directions of the introduction of intelligent infocommunication systems in the course of performance of building-technical expertise and expert researches are outlined. The architecture of Intelligent Support System Building-Technical Expertise and the communication scheme of experts with the system are shown. To mapping expert knowledge formalized in the form of fuzzy associative rules to the memory card of the Cascade ARTMAP category fuzzy artificial neural network, it is proposed to use a fuzzy Mamdani-type inference system. The main input data, on the basis of which a fuzzy conclusion is realized to establish the degree of influence of various environmental factors on the technical condition of buildings and structures, are systematized and presented in a form acceptable for processing by computerized systems. At the same time, the main focus is on the study of facilities that are built and operated on subsidence loess soils. The process of formalization of heuristics, which is based on the formation of associations related to information on the position of signs of deterioration of the technical condition of the objects of expertise and the position of the changed soil, is described. Examples of interpretation and fuzzification of input information on soil properties, characteristics of the soil base of the object of building-technical expertise, and the surrounding area are given. The described approach provides an opportunity to reduce the risks of making wrong decisions by using the system as an intelligent database. The use of an artificial fuzzy neural network of the Cascade ARTMAP category gives the system the ability to form an expert conclusion on the degree of influence of various environmental factors on the technical condition of objects in the fuzzy conditions of a partially observed environment.
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Abbasipayam, Sadjad, and Nataliya Vladislavovna Makrova. "Fuzzy logic and intelligent control of engineering systems of buildings." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2022, no. 1 (January 31, 2022): 22–32. http://dx.doi.org/10.24143/2073-5529-2022-1-22-32.
Повний текст джерелаАнотація:
Energy consumption factors in the systems of cooling, heating, air conditioning and lighting in a building have a significant impact on the energy costs. Intelligent energy control methods help modernize the engineering systems of buildings, while using artificial neural networks and fuzzy logic for minimizing energy consumption is espe-cially effective in the operation of buildings. To control energy consumption there was proposed the Mamdani fuzzy inference system, selected membership functions of Gaussian, triangular and trapezoidal shapes in the course of the research, implemented the types and functions of inputs and outputs for engineering systems control subsystems in software. According to the input and output parameters, the following systems were designed: lighting, smart window, HVAC; fuzzy inference tables were built, graphical data analysis was performed. The proposed control solutions for the implementation of fuzzy rules based on linguistic variables make it possible to adapt the building management system to environmental conditions and prevent excessive energy consumption. The study substantiates the choice of energy-consuming parts of the building; when forming control actions, fuzzy logic rules are applied in functional ranges. The fuzzy inference system was shown to generate the solutions in accordance with changing input data, integrated control is implemented, the responses of lighting, heating, ventilation and air conditioning systems are analyzed depending on the input membership function. It is proposed to control the intensity of ambient light using motion sensors, including optical ones. It is shown that the results obtained make it possible to achieve a reduction in lighting energy consumption by 15 - 25%, maximum use of external light, ensuring a comfortable temperature regime, and also lead to implementing the coordinated and integrated control functions