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Predicting the technical condition of the power transformer using fuzzy logic and dissolved gas analysis method

Power transformers are one of the most important and complex parts of an electric power system. Maintenance is performed for this responsible part based on the technical condition of the transformer using a predictive approach. The technical condition of the power transformer can be diagnosed using a range of different diagnostic methods, for example, analysis of dissolved gases (DGA), partial discharge monitoring, vibration monitoring, and moisture monitoring. In this paper, the authors present a digital model for predicting the technical condition of a power transformer and determining the type of defect and its cause in the event of defect detection. The predictive digital model is developed using the programming environment in LabVIEW and is based on the fuzzy logic approach to the DGA method, interpreted by the key gas method and the Dornenburg ratio method. The developed digital model is verified on a set of 110 kV and 220 kV transformers of one of the sections of the distribution network and thermal power plant in the Russian Federation. The results obtained showed its high efficiency in predicting faults and the possibility of using it as an effective computing tool to facilitate the work of the operating personnel of power enterprises.

An adaptive fuzzy logic control of green tea fixation process based on image processing technology

Design of maximum power point tracking system based on single ended primary inductor converter using fuzzy logic controller, ranking novel extraction systems of seedless barberry (berberis vulgaris) bioactive compounds with fuzzy logic-based term weighting scheme, new analytical assessment for fast and complete pre-fault restoration of grid-connected fswts with fuzzy-logic pitch-angle controller, fuzzy logic supervisor-based novel energy management strategy reflecting different virtual power plants, cooperation of large-scale wind farm and battery storage in frequency control: an optimal fuzzy-logic based controller, an optimal washout filter for motion platform using neural network and fuzzy logic, fuzzy logic-model predictive control energy management strategy for a dual-mode locomotive, coupling geographic information system integrated fuzzy logic-analytical hierarchy process with global and machine learning based sensitivity analysis for agricultural suitability mapping, export citation format, share document.

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Fuzzy logic systems and medical applications

The combination of Artificial Neural Networks and Fuzzy Logic Systems enables the representation of real-world problems via the creation of intelligent and adaptive systems. By adapting the interconnections between layers, Artificial Neural networks are able to learn. A computing framework based on the concept of fuzzy set and rules as well as fuzzy reasoning is offered by fuzzy logic inference systems. The fusion of the aforementioned adaptive structures is called a “Neuro-Fuzzy” system. In this paper, the main elements of said structures are examined. Researchers have noticed that this fusion could be applied for pattern recognition in medical applications.

1. Introduction

This paper highlights the potential uses of fuzzy network structures in the field of medicine and in particular, it focuses on the several methods in which those system in combination with fuzzy logic techniques could be utilized in order to enhance pattern recognition efficiency. Although medicine and control engineering are not directly related, the use of accessible control techniques for on-line devices, particularly in cases of surgical operations and in intensive care units is now feasible. Currently, the application areas of control engineering in medicine range from simple dosage prescription schemes to highly sophisticated adaptive controllers. Real world knowledge can be regarded as incomplete, inaccurate, and inconsistent. Exact medical entities such as fuzzy-sets can be explained by fuzzy logic theory [1] . As it will be reviewed in the following sections, studies have shown that fuzzy logic methodologies can be utilized in early diagnosis of diseases such as Parkinson's disease. Early diagnosis has been proven to be very valuable in creating a more effective treatment plan. Therefore, identifying a method that would allow for early disease diagnosis would be extremely beneficial for the patients. The main contribution of this paper is to analyze various types of fuzzy systems and examine their potential applications in early diagnosis or disease classification.

2. Biological and artificial neural networks

The attempts to substitute certain brain cognitive functions by a computer system are not hindered by the existing differences between the structure of the human brain and that of a computer. Artificial Intelligence is employed for the creation and application of systems that imitate not only logical thinking and behavior but also human intelligence [2] .

A number of issues linked with the evolutionary theory arose, when the idea that the human mind could be perceived as a computer whose processes are observed via reverse engineering was formed. The evolutionary theory states that living species evolve over time [3] . However, a series of adaptive variations result in certain evolutionary changes regarding brain functions. Computer simulation that uses computational models consisting of mathematical equations are utilized for the research of cognitive function processes [4] . Such models include but are not limited to artificial neural networks, which as the name suggests were inspired by biological neural networks. In biology, a neuron is the smallest part of the brain and it constitutes the basic difference between animals and plants (plants do not have neurons). A neuron's main function is to process information. In the cortex of the brain there are approximately 10 billion neurons and 60 trillion connections. As it is shown in Figure 1 , the main sections of a neuron include the body, the axis and the dendrites, which receive signals from neighboring neurons [5] .

3. Fuzzy logic

Fuzzy logic–fuzzy systems comprise one of the three pillars of Computational intelligence which in turn is categorised under the broad field of artificial intelligence. The other two pillars are artificial neural networks and evolutionary computing (evolutionary computation). Fuzzy systems, which utilize fuzzy sets and fuzzy logic, are an attempt to effectively describe the uncertainty of the real world. Fuzzy logic is a generalisation of classical logic and provides mechanisms of approximation (approximate reasoning) and inference (decision making). The approximate reasoning is an attempt to model the human way of thinking and inference, as it is known that the human brain performs more approximate considerations based on qualitative criteria of perception than accurate considerations based on a plethora of data [1] .

A statement can be true “with some degree of truth” [1] , and not just true or false as Boolean logic suggests, the logic on which the modern computer is based on.

Dr. Lutfi Zadeh of the University of California at Berkeley in the 1960s was the first to introduce the concept of fuzzy logic. Fuzzy logic includes 0 and 1 as extreme cases of truth but also incorporates intermediate states of truth [1] . Fuzzy logic resembles the way human brains work.

4. Fuzzy neural networks

The development of a fuzzy system with high-performance is not easily accomplished. Several problems arise, including the search of membership functions and appropriate rules, a process which regularly leads in errors. As a result, the learning algorithms were also applied to fuzzy systems. Neural networks, were considered as an alternate way to automate the development of fuzzy systems [6] . The functions of neural networks include but are not limited to process control applications, data analysis and classification, detection of imperfections, and support to decision-making.

Neural networks and fuzzy systems can be fused in order to increase their advantages and to decrease their shortcomings. Neural network learning techniques can be utilized in order to substantially reduce the development time of fuzzy systems as well as the cost while improving the performance rates [7] . Figures 2 and ​ and3 3 present two potential models of fuzzy neural systems. In Figure 2 , the fuzzy interface block provides an input vector to a multi-layer neural network as a response to linguistic statements. Subsequently, the neural network is trained to generate required outputs or decisions [8] .

In the second case, the fuzzy inference mechanism is determined by a multi-layered neural network.

The computational characteristics of learning offered by neural networks are obtained by fuzzy systems and in return, neural networks receive the interpretation and clarity of systems representation [9] . A fuzzy neural network or neuro-fuzzy system (NFS) utilizes approximation techniques acquired from neural networks, in order to identify parameters of a fuzzy system (i.e., fuzzy sets, fuzzy rules).

5. Neuro-Fuzzy systems categories

5.1. cooperative neuro-fuzzy system.

For the model of cooperative neural fuzzy systems as shown in Figures 4 and ​ and5, 5 , the artificial neural network (ANN) and fuzzy system work independently. The ANN tries to learn the parameters from the fuzzy system, a process that can be performed either offline or online [8] .

In the upper left example of Figure 4 , the fuzzy rules provided by the training data combined with the fuzzy sets are utilized to form the fuzzy system (offline determination).

In the upper right model of Figure 4 , the fuzzy neural network learns the fuzzy sets from the given training data (offline determination). As it is shown in Figure 4 , in the lower left neuro-fuzzy case, the fuzzy rules and membership functions must be defined beforehand, in order for the system to learn all membership function parameters online. For the improvement of the learning step, the error has to be measured. In the lower right model, a rule weight which is interpreted as the influence of a rule, is determined for all fuzzy rules by a neural network (both online and offline determination) [8] .

A cooperative system only utilizes neural networks in an initial phase. The neural networks using training data, establish sub-blocks of the fuzzy system. Subsequent removal occurs, resulting in the implementation of only the fuzzy system [6] .

5.2. Concurrent Neuro-Fuzzy system

In the concurrent neuro-fuzzy system ( Figure 6 ), the neural network and the fuzzy system constantly function in a collective manner, with the neural network pre-processing the inputs of the fuzzy system.

5.3. Hybrid Neuro-Fuzzy system

Hybrid neuro-fuzzy systems ( Figure 7 ) utilize neural networks in order to identify certain parameters of a fuzzy system. In this case, the architecture of hybrid NFS offers a great advantage seeing as the fuzzy system and neural network do not have to communicate with each other. In addition, these systems can learn online and offline [6] .

6. Fuzzy systems in medicine

Due to their effectiveness, AI (artificial intelligence) techniques, such as fuzzy logic, play a prominent role in the field of medicine. Such methods allow for not only an efficient but also a prompt diagnosis. Guzmán et al., 2019 created a fuzzy classifier in order to perform blood pressure level classification. The main results of this study showed that a type-1 fuzzy inference system or an interval type-2 fuzzy inference system constitute the best architectures to perform said classification [10] . Fuzzy logic has been also applied in order to provide risk assessment for hypertension. Melin, Miramontes and Prado-Arechiga, 2018, designed a model that combined neural networks and fuzzy logic for this purpose. Fuzzy systems were a key part of this study since they regulated the classification uncertainty. This hybrid model provided good results with excellent performance regarding its task [11] . Studies have also shown that fuzzy systems can be applied in Parkinson's diagnosis. Abiyev and Abizade, 2016, proposed a system for Parkinson's disease diagnosis based on the fusion of the fuzzy system and neural networks. The proposed fuzzy neural system (FNS) allows for efficient classification of healthy individuals, a fact that was established through simulation of the system using data obtained from UCI machine learning repository [12] .

Another study tested the technique of classifying medical data sets by constructing fuzzy inference systems or fuzzy expert systems. The analysis of data related to Parkinson's yielded a large amount of information. In order to further study and explore the information provided, clinical observations, and disease diagnosis were mathematically translated. Knowledge-based Systems in combination with Data Mining tools and a fuzzy decision maker as well as Artificial Neural Networks Classifiers proved to be useful techniques for mapping clinical data to a numerical data set by exploiting a set of rules [13] .

Kaur et al., 2017, described Parkinson's disease using an adaptive neuro-fuzzy technique. According to their results, the adaptive neuro fuzzy expert system showed higher accuracy rates than the fuzzy expert system. In addition, the adaptive neuro fuzzy expert system exhibited higher rate of sensitivity, specificity, and precision when compared to a fuzzy expert system [14] .

7. Discussion and future work

In this paper, “fuzzy logic” systems which could be used to formalize approximate reasoning in medical diagnostic systems are described. The potential implementation of fuzzy artificial networks in medicine is also analyzed. Authors further work would focus on applying the aforementioned techniques for the establishment of intelligent systems that could be utilized in disease treatment and diagnosis. In more detail, future steps would include the development of a fuzzy expert system that would be utilized in PD diagnosis. This study would include experiments that would evaluate parameters such as accuracy, sensitivity, and specificity.

Conflicts of interest: The authors have no conflicts of interest.

Introduction to fuzzy logic

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77 Logic Essay Topic Ideas & Examples

🏆 best logic topic ideas & essay examples, 📌 simple & easy logic essay titles, 👍 good essay topics on logic.

• Language and Logic: The Similarities and Differences A major function of language is that the symbols are subjective. There are various areas of study that will allow one to get the right interpretation of language and logic.
• Rene Descartes: Education and Rules of Logic I believe it is a considerable drawback of schooling, and it should be fixed in the near future, as young adults need to learn how to apply the knowledge they get. We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
• Logic and Philosophy Relations Aristotle is reputed to be the first man to study the logic concept although there have been other numerous contributions to the concept over the years.
• Relational Logic in “I-It” and “I-You” Relations While considering the concept of “I-It”, specific attention should be paid to the perception of the self through It unless a person is not involved in relation with another thing or object.
• Say “Stop” to Childhood Obesity: Logic Model The company is related to the priority population since it aims at reducing the rates of childhood obesity among Hispanic children.
• History of Logic: Brief Review of Inferences or Judgments The history of logic relates to the progress of the science of valid inference. The logic of Aristotle was of importance during the period of the Renaissance too.
• Logic in Islam and Number of Islamic Theologians Combination of the diverse philosophical ideologies resulted into Islamic logic, which has made marked contribution in the Islamic philosophy.”Historians of logic have long recognized that the medieval Muslim philosophers and philosophical theologians rendered variously as […]
• Propositional and First-Order Logic in Artificial Intelligence Artificial intelligence’s propositional logic analyzes sentences as variables, and in the event of complicated sentences, the first phase is to deconstruct the sentence into its component variables.
• Logic and Statistical Significance This week, we were asked to evaluate the footnote, which stated that due to the fact that the research was explanatory, the level of significance was relaxed to 0.1.
• Aristotle’s View on the Concept of Logic Thus, it was shown that logic is not just a specific doctrine of specific things or terms, but the science of the laws of syllogisms, such as modus ponens or modus tollens, expressed in variables. […]
• Is Female Thinking and Logic Truly Different From the Male’s One It is necessary to analyze this question from a scientific point of view and to understand whether the thought processes of different genders are different.
• Mathematical Platonism: Philosophy’s Loss of Logic In 1953, Gottlob Frege posted a strong argument that the language of mathematics tends to refer to and quantify the mathematical objects and the corresponding theories are true. Frege argues that mathematical language is quantifiable, […]
• Logic and Design: Flowcharts and Pseudocode The basic understanding of logic and design is that processes should be presented in a way that demonstrates certain algorithms, i.e.the description of a process should be precise and should contain detailed instructions on what […]
• Feelings and Logic in the Literature Works In his short story, Poe covers the side of the senses and the rigor of the mind. Another metaphor is the combination of the heart and the clock that beat in the head of the […]
• Dangers of Logic and Artificial Intelligence The following are the dangers of logic and artificial intelligence when applied in various areas. The last danger of logic and artificial intelligence relates to autonomous weapons.
• Postmodernism, or, the Cultural Logic of Late Capitalism I agree with the statement because people with different cultures have different ways of doing things and architecture is one of the crucial tools used to express the culture of the people.
• Komatsu Company’s Service-Dominant Logic In this case, the company continues to focus on providing unique solutions to customers, but it has more opportunities for development, innovation, and addressing customers’ needs with the help of a new model elements.
• NGO Logic Model: Review The successful implementation of the proposed project depends on the stakeholders’ ability to be involved and focus on the anticipated short-term and long-term goals.
• Importance to Reason and Logic Prior to evaluating the strengths and weaknesses of reason as a way of knowing, we should first discuss such concept as knowledge, because even now philosophers and scholars have not come to the agreement as […]
• Logic Dialectic and Rhetoric: Compare and Contrast In addition, the prominent thinker estimated rhetoric in the context of logic, because logic, as well as rhetoric and dialectic, point out the studying of persuasion methods.
• Women, Instagram and Calligraphy: Neoliberal Logic in Production of Aesthetic Objects Such a reality imposes the need for the research of a valuable topic that deals with the role of women in the creation of aesthetic content for online commerce on social media.
• The Use of Logic in the Declaration of Independence: Following Jefferson’s Argument By emphasizing the notions of egalitarianism and the principles of natural law, Jefferson successfully appeals to logic and makes a convincing presentation of the crucial social and legal principles to his opposition.
• Logic and Philosophy Questions As a rule, a traditional logical inference has two basic elements, i.e, a premise and a conclusion. Therefore, A.
• The Logic of Modern Physics The purpose of this paper is to reflect on the writings of these three scholars and generate three questions that can be discussed in class.
• Radix Sort Algorithm, Its Logic and Applications The sorting process starts from the rightmost digit based on the key or the positions of the numbers being sorted. LSD radix sorts the integers from the least to the most significant digit.
• Work and Family: Institutional Logic The recognition of the practical and theoretical benefits of the institutional approach led to the creation of the notion of institutional logic, which comprises “the socially constructed, historical patterns of material practices, assumptions, values, beliefs, […]
• Yield Management and Service Dominant Logic The reduction in the price of the goods offered means that loyal customer are now able to enjoy the product during different seasons in a year.
• The Logic of Using Quantitative Data As far as the types of quantitative data required to show the results of an intervention are concerned, it can be suggested that the information including the grades that the students receive for their performance, […]
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• Programming Logic and Design – Program Change In the online processing method, processing of data takes place as it is input into the program, that is, unlike in batch processing it does not wait for the data to be organized into a […]
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• The Logic: Model and Evaluation At the initiation stage of the project, the targeted indicators and deliverables of the project are s sufficiently drawn by the project staff according to the basic needs assessments already conducted.
• Understanding Economics: The Nature and Logic of Capitalism These profits are determined by the prices of the commodities and the cost of production that the producer incurred during the whole process of production and creation of goods and services[3].
• Analyzing the Logic of an Article: Cultural Authenticity and Recovery Maintenance in a Rural First Nation Community The key question of the article is how culture may bolster resilience in substance abuse recovery as well as what constitutes “cultural authenticity” for both indigenous and non-indigenous residents of a remote community.
• Informal Logic-Fallacies Definition Syntactic ambiguity is the second type of ambiguity and is normally identified by the presence of ambiguous grammar usage or the general structure of the statement. Hence, the ambiguity of this sentence is in the […]
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• What Is The Fundamental Economic Logic Of Minoli’s Turnaround
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• The Role Of Cognitive Development, Logic, And Emotionality
• Understanding the Logic of Learned Education
• What Logic Was Forwarded by Schwcitzguebel in Support of Tourism
• The Sanctions Debate and the Logic of Choice/Diplomacy
• The Theory of Fuzzy Logic and its Application to Real Estate Valuation
• The Notion of Hyperreality in Frederic Jameson’s Cultural Logic of Late Capitalism
• Use Of Logic To Seduce Women In John Donne’s ‘The Flea’ And Andrew Marvell’s ‘To His Coy Mistress’
• The World Religion Dataset, 1945–2010: Logic, Estimates, and Trends
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• The Reason And Logic Behind The Law
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• The Nature and Logic of Capitalism by Heilbroner
• The Political-Economic Logic of World Governance
• The New Growth Theory: Its Logic and Trade Policy Implications
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A type-2 fuzzy review topic-based model for personalized recommendation

• Published: 09 April 2024

Cite this article

• Cong Wang   ORCID: orcid.org/0000-0002-5300-0122 1 ,
• Yansong Shi 2 &
• Guoqing Chen 2  

Recommender systems are becoming increasingly indispensable for e-commerce. To achieve interpretable recommendation, review topic-based recommendation is an important research area that aims to infer users’ ratings over their unrated items using existing reviews and corresponding ratings simultaneously. However, combining latent factors and review topics can also introduce uncertainties in the model learning process, both in inference and sampling. To address this challenge, we propose a new model called type-2 fuzzy review topic-based recommendation (T2FR). T2 fuzzy membership functions are introduced to represent the topic parameter uncertainties, and a strategy of dual sampling is developed to deal with the topic T2 membership functions and further used for uncertainty handling. Abundant experiments on data collected from real-world e-commerce platforms demonstrate the effectiveness of the proposed model compared with baseline methods in terms of rating prediction accuracy with interpretation. Our proposed T2FR can substantially benefit both e-commerce platforms and consumers.

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https://www.alliedmarketresearch.com/recommendation-engine-market-A14635 .

Li, S. S., & Karahanna, E. (2015). Online recommendation systems in a b2c e-commerce context: a review and future directions. J. Assoc. Inf. Syst., 16 (2), 2.

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Acknowledgement

This work is partially supported by  National Natural Science Foundation of China (No. 72101007, 12271012, 72131001).

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Wang, C., Ma, Y., Shi, Y. et al. A type-2 fuzzy review topic-based model for personalized recommendation. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09829-2

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Accepted : 05 March 2024

Published : 09 April 2024

DOI : https://doi.org/10.1007/s10660-024-09829-2

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