The analysis of the operation of tourism companies will provide valid information for the design of policies to reactivate the tourism industry, which has been strongly affected during the pandemic generated by COVID-19. The objective of this paper is to use soft computing techniques to analyse tourism companies in Ecuador. First of all, dimensionality reduction methods are applied: principal component analysis, isometric feature mapping and locally linear embedding, on data of tourism enterprises in Ecuador for the year 2015. In addition, to verify the trend of operational variables, the data of tourism companies in Ecuador in 2019 and 2020 are analysed with dimensionality reduction methods that improve the interpretation by minimizing the loss of information. The data sets are analysed with k-means, k-medoids and Hierarchical Clustering, generating groups according to similar characteristics. The optimal number of clusters is determined with the following: Elbow Method, Silhouette Coefficient, Davies-Bouldin Index and Dunn Index. In addition, an analysis of the operation of tourism companies in the year 2020 concerning previous years is included. The study allows exploring Soft Computing techniques to identify important information for the definition of strategies that contribute to an effective reactivation of the tourist industry of Ecuador.
{"title":"Exploratory techniques to analyse Ecuador's tourism industry","authors":"Anita Herrera, Ángel Arroyo, Alfredo Jiménez, Álvaro Herrero","doi":"10.1093/jigpal/jzae040","DOIUrl":"https://doi.org/10.1093/jigpal/jzae040","url":null,"abstract":"The analysis of the operation of tourism companies will provide valid information for the design of policies to reactivate the tourism industry, which has been strongly affected during the pandemic generated by COVID-19. The objective of this paper is to use soft computing techniques to analyse tourism companies in Ecuador. First of all, dimensionality reduction methods are applied: principal component analysis, isometric feature mapping and locally linear embedding, on data of tourism enterprises in Ecuador for the year 2015. In addition, to verify the trend of operational variables, the data of tourism companies in Ecuador in 2019 and 2020 are analysed with dimensionality reduction methods that improve the interpretation by minimizing the loss of information. The data sets are analysed with k-means, k-medoids and Hierarchical Clustering, generating groups according to similar characteristics. The optimal number of clusters is determined with the following: Elbow Method, Silhouette Coefficient, Davies-Bouldin Index and Dunn Index. In addition, an analysis of the operation of tourism companies in the year 2020 concerning previous years is included. The study allows exploring Soft Computing techniques to identify important information for the definition of strategies that contribute to an effective reactivation of the tourist industry of Ecuador.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"113 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, we want to demonstrate how thoughts experiments (TEs) incorporate cognitive structures—abductive inferences as conceptual metaphors—that reliably underpin everyday thinking and are enhanced and rendered more effective in scientific and philosophical contexts. Indeed one might successfully rethink the inferential structure at the heart of thought experiment production as the application of a generative abductive procedure. We shall characterize TES as possessing two characteristics that are essential to the definitions of abductive and metaphorical thinking, but when considered in relation to TE’s description, can excuse mild bewilderment: both knowledge-enhancing and ignorance-preserving features. In sum, we will say that TEs realize extended conceptual metaphors, which instantiate forms of abductive reasoning and, therefore, partially preserve the ignorance of the authors who produce them (even if they also increase a bit their knowledge by—so to speak—mitigating ignorance). In certain fortunate and exceptional instances, however, TEs can also provide a purely knowledge-enhancing benefit; in order to do this, a reference to the innovative and creative function of thought experiments in Galileo’s findings is also included.
{"title":"Model-based abductive cognition: What thought experiments teach us","authors":"Lorenzo Magnani, Selene Arfini","doi":"10.1093/jigpal/jzae096","DOIUrl":"https://doi.org/10.1093/jigpal/jzae096","url":null,"abstract":"In this article, we want to demonstrate how thoughts experiments (TEs) incorporate cognitive structures—abductive inferences as conceptual metaphors—that reliably underpin everyday thinking and are enhanced and rendered more effective in scientific and philosophical contexts. Indeed one might successfully rethink the inferential structure at the heart of thought experiment production as the application of a generative abductive procedure. We shall characterize TES as possessing two characteristics that are essential to the definitions of abductive and metaphorical thinking, but when considered in relation to TE’s description, can excuse mild bewilderment: both knowledge-enhancing and ignorance-preserving features. In sum, we will say that TEs realize extended conceptual metaphors, which instantiate forms of abductive reasoning and, therefore, partially preserve the ignorance of the authors who produce them (even if they also increase a bit their knowledge by—so to speak—mitigating ignorance). In certain fortunate and exceptional instances, however, TEs can also provide a purely knowledge-enhancing benefit; in order to do this, a reference to the innovative and creative function of thought experiments in Galileo’s findings is also included.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"30 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predicting stock markets is a problem that has generated many answers. According to one group of responses, the divergence thesis, it is impossible to accomplish this since the prediction has a ‘bending effect’ that would cause the market to behave in a way that would permanently depart from what was predicted, i.e. the prediction would falsify itself. There are at least three types of impossibility: logical, theoretical and empirical. A second class of responses argues that despite the ‘bending effect’ of predictions, it is still feasible to predicting stock markets. These responses, the convergence thesis, contend that we can achieve it by demonstrating that there are fixed points or that the prediction and market behavior will eventually converge. I expand this line of reasoning by showing that the performativity makes it possible certain predictions by an alignment between the ‘ontic’ and the ‘epistemic’ state of the markets. In addition, I show that performativity enables us to explain how a prediction is produced, why it works initially and then why it fails (i.e. why its predictive power is destroyed).
{"title":"Do predictions destroy predictability? A study focusing on stock markets","authors":"Emiliano Ippoliti","doi":"10.1093/jigpal/jzae091","DOIUrl":"https://doi.org/10.1093/jigpal/jzae091","url":null,"abstract":"Predicting stock markets is a problem that has generated many answers. According to one group of responses, the divergence thesis, it is impossible to accomplish this since the prediction has a ‘bending effect’ that would cause the market to behave in a way that would permanently depart from what was predicted, i.e. the prediction would falsify itself. There are at least three types of impossibility: logical, theoretical and empirical. A second class of responses argues that despite the ‘bending effect’ of predictions, it is still feasible to predicting stock markets. These responses, the convergence thesis, contend that we can achieve it by demonstrating that there are fixed points or that the prediction and market behavior will eventually converge. I expand this line of reasoning by showing that the performativity makes it possible certain predictions by an alignment between the ‘ontic’ and the ‘epistemic’ state of the markets. In addition, I show that performativity enables us to explain how a prediction is produced, why it works initially and then why it fails (i.e. why its predictive power is destroyed).","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"7 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Computer scientists, logicians and other researchers have recently paid renewed attention to the model of computation based on the logic of combinators. We develop a new formalization of the syntax of combinators employing the ‘generic figures’ approach to the categories of presheaves that provides an intuitive and easily interpreted diagrammatic syntax for combinators, one that eschews the need to label proper combinators with variable names. Furthermore, we show that this formal framework provides the possibility of representing choices of operand from among given alternatives at each level of a combinatory term, allowing for a more general type of combinatory expression, which we call a multi-combinator.
{"title":"Combinators as presheaves","authors":"Rocco Gangle, Fernando Tohmé, Gianluca Caterina","doi":"10.1093/jigpal/jzae097","DOIUrl":"https://doi.org/10.1093/jigpal/jzae097","url":null,"abstract":"Computer scientists, logicians and other researchers have recently paid renewed attention to the model of computation based on the logic of combinators. We develop a new formalization of the syntax of combinators employing the ‘generic figures’ approach to the categories of presheaves that provides an intuitive and easily interpreted diagrammatic syntax for combinators, one that eschews the need to label proper combinators with variable names. Furthermore, we show that this formal framework provides the possibility of representing choices of operand from among given alternatives at each level of a combinatory term, allowing for a more general type of combinatory expression, which we call a multi-combinator.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"116 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Knowledge representation and reasoning (KRR) is a fundamental area in artificial intelligence (AI) research, focusing on encoding world knowledge as logical formulae in ontologies. This formalism enables logic-based AI systems to deduce new insights from existing knowledge. Within KRR, description logics (DLs) are a prominent family of languages to represent knowledge formally. They are decidable fragments of first-order logic, and their models can be visualized as edge- and vertex-labeled directed binary graphs. DLs facilitate various reasoning tasks, including checking the satisfiability of statements and deciding entailment. However, a significant challenge arises in the computation of models of DL ontologies in the context of explaining reasoning results. Although existing algorithms efficiently compute models for reasoning tasks, they usually do not consider aspects of human cognition, leading to models that may be less effective for explanatory purposes. This paper tackles this challenge by proposing an approach to enhance the intelligibility of models of DL ontologies for users. By integrating insights from cognitive science and philosophy, we aim to identify key graph properties that make models more accessible and useful for explanation.
{"title":"Involving cognitive science in model transformation for description logics","authors":"Willi Hieke, Sarah Schwöbel, Michael N Smolka","doi":"10.1093/jigpal/jzae088","DOIUrl":"https://doi.org/10.1093/jigpal/jzae088","url":null,"abstract":"Knowledge representation and reasoning (KRR) is a fundamental area in artificial intelligence (AI) research, focusing on encoding world knowledge as logical formulae in ontologies. This formalism enables logic-based AI systems to deduce new insights from existing knowledge. Within KRR, description logics (DLs) are a prominent family of languages to represent knowledge formally. They are decidable fragments of first-order logic, and their models can be visualized as edge- and vertex-labeled directed binary graphs. DLs facilitate various reasoning tasks, including checking the satisfiability of statements and deciding entailment. However, a significant challenge arises in the computation of models of DL ontologies in the context of explaining reasoning results. Although existing algorithms efficiently compute models for reasoning tasks, they usually do not consider aspects of human cognition, leading to models that may be less effective for explanatory purposes. This paper tackles this challenge by proposing an approach to enhance the intelligibility of models of DL ontologies for users. By integrating insights from cognitive science and philosophy, we aim to identify key graph properties that make models more accessible and useful for explanation.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"7 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141935539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In 2023, Kuznetsov and Speranski introduced infinitary action logic with multiplexing $!^{m}nabla textrm{ACT}_{omega }$ and proved that the derivability problem for it lies between the $omega $ level and the $omega ^{omega }$ level of the hyperarithmetical hierarchy. We prove that this problem is $varDelta ^{0}_{omega ^{omega }}$-complete under Turing reductions. Namely, we show that it is recursively isomorphic to the satisfaction predicate for computable infinitary formulas of rank less than $omega ^{omega }$ in the language of arithmetic. As a consequence we prove that the closure ordinal for $!^{m}nabla textrm{ACT}_{omega }$ equals $omega ^{omega }$. We also prove that the fragment of $!^{m}nabla textrm{ACT}_{omega }$ where Kleene star is not allowed to be in the scope of the subexponential is $varDelta ^{0}_{omega ^{omega }}$-complete. Finally, we present a family of logics, which are fragments of $!^{m}nabla textrm{ACT}_{omega }$, such that the complexity of the $k$-th logic lies between $varDelta ^{0}_{omega ^{k}}$ and $varDelta ^{0}_{omega ^{k+1}}$.
{"title":"Hyperarithmetical complexity of infinitary action logic with multiplexing","authors":"Tikhon Pshenitsyn","doi":"10.1093/jigpal/jzae078","DOIUrl":"https://doi.org/10.1093/jigpal/jzae078","url":null,"abstract":"In 2023, Kuznetsov and Speranski introduced infinitary action logic with multiplexing $!^{m}nabla textrm{ACT}_{omega }$ and proved that the derivability problem for it lies between the $omega $ level and the $omega ^{omega }$ level of the hyperarithmetical hierarchy. We prove that this problem is $varDelta ^{0}_{omega ^{omega }}$-complete under Turing reductions. Namely, we show that it is recursively isomorphic to the satisfaction predicate for computable infinitary formulas of rank less than $omega ^{omega }$ in the language of arithmetic. As a consequence we prove that the closure ordinal for $!^{m}nabla textrm{ACT}_{omega }$ equals $omega ^{omega }$. We also prove that the fragment of $!^{m}nabla textrm{ACT}_{omega }$ where Kleene star is not allowed to be in the scope of the subexponential is $varDelta ^{0}_{omega ^{omega }}$-complete. Finally, we present a family of logics, which are fragments of $!^{m}nabla textrm{ACT}_{omega }$, such that the complexity of the $k$-th logic lies between $varDelta ^{0}_{omega ^{k}}$ and $varDelta ^{0}_{omega ^{k+1}}$.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"51 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141532478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luis Hernández-Álvarez, Juan José Bullón Pérez, Araceli Queiruga-Dios
Smart grids are designed to revolutionize the energy sector by creating a smarter, more efficient and reliable power supply network. The rise of smart grids is a response to the need for a more comprehensive and sophisticated energy system that caters to the needs of homes and businesses. Key features of smart grids include the integration of renewable energy sources, decentralized generation and advanced distribution networks. At the heart of smart grids is a sophisticated metering system, consisting of intelligent electronic devices that measure energy consumption and enable two-way communication between the utility company and the consumer. The information exchanged via these devices is critical, as it includes sensitive data such as energy consumption patterns and billing information. With the rise of the Internet of Things (IoT) and Industry 4.0, the security of this information is of utmost importance. This paper delves into the security challenges faced by advanced metering infrastructure (AMI) and highlights the crucial role played by cryptography in ensuring the secure exchange of sensitive information. Cryptographic protocols such as encryption, authentication and digital signatures are essential components of a secure smart grid infrastructure. These protocols work together to ensure the confidentiality, integrity and authenticity of the information exchanged between the utility company and the consumer.
{"title":"Security in advanced metering infrastructures: Lightweight cryptography","authors":"Luis Hernández-Álvarez, Juan José Bullón Pérez, Araceli Queiruga-Dios","doi":"10.1093/jigpal/jzae074","DOIUrl":"https://doi.org/10.1093/jigpal/jzae074","url":null,"abstract":"Smart grids are designed to revolutionize the energy sector by creating a smarter, more efficient and reliable power supply network. The rise of smart grids is a response to the need for a more comprehensive and sophisticated energy system that caters to the needs of homes and businesses. Key features of smart grids include the integration of renewable energy sources, decentralized generation and advanced distribution networks. At the heart of smart grids is a sophisticated metering system, consisting of intelligent electronic devices that measure energy consumption and enable two-way communication between the utility company and the consumer. The information exchanged via these devices is critical, as it includes sensitive data such as energy consumption patterns and billing information. With the rise of the Internet of Things (IoT) and Industry 4.0, the security of this information is of utmost importance. This paper delves into the security challenges faced by advanced metering infrastructure (AMI) and highlights the crucial role played by cryptography in ensuring the secure exchange of sensitive information. Cryptographic protocols such as encryption, authentication and digital signatures are essential components of a secure smart grid infrastructure. These protocols work together to ensure the confidentiality, integrity and authenticity of the information exchanged between the utility company and the consumer.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"88 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141530092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. From the current literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively.
{"title":"Machine overstrain prediction for early detection and effective maintenance: A machine learning algorithm comparison","authors":"Bruno Mota, Pedro Faria, Carlos Ramos","doi":"10.1093/jigpal/jzae055","DOIUrl":"https://doi.org/10.1093/jigpal/jzae055","url":null,"abstract":"Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. From the current literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"18 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141167699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Let $varSigma $ be a signature without $0$-ary operation symbols and $textsf{Sl}$ the category of semilattices. Then, after defining and investigating the categories $int ^{textsf{Sl}}textrm{Isys}_{varSigma }$, of inductive systems of $varSigma $-algebras over all semilattices, which are ordered pairs $mathscr{A}= (textbf{I},mathscr{A})$ where $textbf{I}$ is a semilattice and $mathscr{A}$ an inductive system of $varSigma $-algebras relative to $textbf{I}$, and PłAlg$ (varSigma )$, of Płonka $varSigma $-algebras, which are ordered pairs $(textbf{A},D)$ where $textbf{A}$ is a $varSigma $-algebra and $D$ a Płonka operator for $textbf{A}$, i.e. in the traditional terminology, a partition function for $textbf{A}$, we prove the main result of the paper: There exists an adjunction, which we call the Płonka adjunction, from $int ^{textsf{Sl}}textrm{Isys}_{varSigma }$ to PłAlg$ (varSigma )$.
{"title":"Płonka adjunction","authors":"J Climent Vidal, E Cosme Llópez","doi":"10.1093/jigpal/jzae064","DOIUrl":"https://doi.org/10.1093/jigpal/jzae064","url":null,"abstract":"Let $varSigma $ be a signature without $0$-ary operation symbols and $textsf{Sl}$ the category of semilattices. Then, after defining and investigating the categories $int ^{textsf{Sl}}textrm{Isys}_{varSigma }$, of inductive systems of $varSigma $-algebras over all semilattices, which are ordered pairs $mathscr{A}= (textbf{I},mathscr{A})$ where $textbf{I}$ is a semilattice and $mathscr{A}$ an inductive system of $varSigma $-algebras relative to $textbf{I}$, and PłAlg$ (varSigma )$, of Płonka $varSigma $-algebras, which are ordered pairs $(textbf{A},D)$ where $textbf{A}$ is a $varSigma $-algebra and $D$ a Płonka operator for $textbf{A}$, i.e. in the traditional terminology, a partition function for $textbf{A}$, we prove the main result of the paper: There exists an adjunction, which we call the Płonka adjunction, from $int ^{textsf{Sl}}textrm{Isys}_{varSigma }$ to PłAlg$ (varSigma )$.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"48 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The generation of the pitch control signal in a wind turbine (WT) is not straightforward due to the nonlinear dynamics of the system and the coupling of its internal variables; in addition, they are subjected to the uncertainty that comes from the random nature of the wind. Fuzzy logic has proved useful in applications with changing system parameters or where uncertainty is relevant as in this one, but the tuning of the fuzzy logic controller (FLC) parameters is neither straightforward nor an easy task. On the other hand, reinforcement learning (RL) allows systems to automatically learn, and this capability can be exploited to tune the FLC. In this work, a WT pitch control architecture that uses RL to tune the membership functions and scale the output of a fuzzy controller is proposed. The RL strategy calculates the fuzzy controller gains in order to reduce the output power error of the WT according to the wind speed. Different reward mechanisms based on the output power error have been considered. Simulation results with different wind profiles show that this architecture performs better (123.7 W) in terms of power errors than an FLC without RL (133.2 W) or a simpler PID (208.8 W). Even more, it provides a smooth response and outperforms other hybrid controllers such as RL-PID and radial basis function neural network control.
{"title":"Combination of fuzzy control and reinforcement learning for wind turbine pitch control","authors":"J Enrique Sierra-Garcia, Matilde Santos","doi":"10.1093/jigpal/jzae054","DOIUrl":"https://doi.org/10.1093/jigpal/jzae054","url":null,"abstract":"The generation of the pitch control signal in a wind turbine (WT) is not straightforward due to the nonlinear dynamics of the system and the coupling of its internal variables; in addition, they are subjected to the uncertainty that comes from the random nature of the wind. Fuzzy logic has proved useful in applications with changing system parameters or where uncertainty is relevant as in this one, but the tuning of the fuzzy logic controller (FLC) parameters is neither straightforward nor an easy task. On the other hand, reinforcement learning (RL) allows systems to automatically learn, and this capability can be exploited to tune the FLC. In this work, a WT pitch control architecture that uses RL to tune the membership functions and scale the output of a fuzzy controller is proposed. The RL strategy calculates the fuzzy controller gains in order to reduce the output power error of the WT according to the wind speed. Different reward mechanisms based on the output power error have been considered. Simulation results with different wind profiles show that this architecture performs better (123.7 W) in terms of power errors than an FLC without RL (133.2 W) or a simpler PID (208.8 W). Even more, it provides a smooth response and outperforms other hybrid controllers such as RL-PID and radial basis function neural network control.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"77 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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