Relevant bibliographies by topics / Gated Continuous Logic Networks

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Gated Continuous Logic Networks'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Gated Continuous Logic Networks"

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Boukadida, Souha, Soufien Gdaim, and Abdellatif Mtiba. "Sensor Fault Detection and Isolation Based on Artificial Neural Networks and Fuzzy Logic Applicated on Induction Motor for Electrical Vehicle." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 2 (June 1, 2017): 601. http://dx.doi.org/10.11591/ijpeds.v8.i2.pp601-611.

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<p>Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical vehicle. Like failures of a position sensor, a voltage sensor, and current sensors. Three-phase induction motors are the “workhorses” of industry and are the most widely used electrical machines. This paper presents a scheme for Fault Detection and Isolation (FDI). The proposed approach is a sensor-based technique using the mains current measurement. Current sensors are widespread in power converters control and in electrical drives. Thus, to ensure continuous operation with reconfiguration control, a fast sensor fault detection and isolation is required. In this paper, a new and fast faulty current sensor detection and isolation is presented. It is derived from intelligent techniques. The main interest of field programmable gate array is the extremely fast computation capabilities. That allows a fast residual generation when a sensor fault occurs. Using of Xilinx System Generator in Matlab / Simulink allows the real-time simulation and implemented on a field programmable gate array chip without any VHSIC Hardware Description Language coding. The sensor fault detection and isolation algorithm was implemented targeting a Virtex5. Simulation results are given to demonstrate the efficiency of this FDI approach.</p>
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Tran, Duc M., Kyungah Kim, and Joon-Young Choi. "CLB-Based Development of BiSS-C Interface Master for Motor Encoders." Electronics 12, no. 4 (February 9, 2023): 886. http://dx.doi.org/10.3390/electronics12040886.

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Encoder interfaces should be operated in real time with high precision and fast processing for industrial motor control systems. The continuous bidirectional serial synchronous (BiSS-C) interface is an open-source serial communication protocol designed for motor encoders and is suitable for industrial purposes because of its fast serial communication speed. In this study, we propose a method for developing a BiSS-C interface master for a motor encoder slave, using only the configurable logic block (CLB) peripheral integrated into TI microcontroller units. By analyzing the detailed operation protocol of the BiSS-C interface, we create the truth and state tables for logic circuits and finite state machines, which are required for the BiSS-C interface master. Then, by programming the CLB based on the created truth and state tables, we implement the master clock, serial peripheral interface (SPI) clock, and operational process for the master. This approach is cost-efficient because additional hardware components, such as a field-programmable gate array or a complex programmable logic device, are not required for the master implementations. The developed method can be immediately applied to developing the masters for other BiSS-C encoders with different specifications, which is certainly necessary for a motor drive development and test. By building an AC motor control system with the developed master and performing various experiments, we verify the performance and practical usefulness of the developed BiSS-C interface master. The maximum master clock frequency without any CRC errors is achieved by 6.25 MHz, which can cope with more than 20 kHz motor control cycle frequency. The usefulness is demonstrated by showing the motor speed and position control performance that are acceptable in real applications.
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KIROLOS, SAMI, and YEHIA MASSOUD. "DYNAMIC VOLTAGE SCALING CONTINUOUS ADAPTIVE-SIZE CELL DESIGN TECHNIQUE." Journal of Circuits, Systems and Computers 17, no. 05 (October 2008): 871–83. http://dx.doi.org/10.1142/s0218126608004630.

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In this paper, we present an adaptive circuit design that is capable of increasing the effective size-ratio of combinational logic gates to extend the balanced operation in the subthreshold region as well as to maintain high performance at the nominal VDD. We optimize the sizes of the PMOS transistors in the pull-up network for minimum power dissipation and propagation delay over a wide range of supply voltage. In addition to the minimized energy operation, the dynamically adjustable gate size-ratio allows the gate to preserve a symmetric voltage transfer characteristic at both normal supply and subthreshold operation, which translates to maximized noise margins. Simulation results show that up to 70.9% reduction in the energy can be achieved for a ring oscillator, as compared to the fixed size design capable of operating under supply voltage in the range of 75 mV to 1.2 V. For designs working under dynamic voltage scaling schemes, our technique presents a very effective and efficient solution for balanced minimum energy operation in the subthreshold region while preserving high performance at the nominal supply voltage.
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Plyusnin, Nikolay. "Tunable logic of complex variables and quantum networks on its basis." Robotics and Technical Cybernetics 10, no. 4 (December 2022): 267–74. http://dx.doi.org/10.31776/rtcj.10404.

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Continuous - additive-multiplicative (AM) logic is considered, in which logical operations are replaced by algebraic operations («×» and «+») or operations with vectors, and binary variables «0» and «1» are replaced by continuous scalar ones («0- 1») or complex variables. To build this logic, a continuous analogue of the canonical form of Boolean logic is used in the form of a perfect disjunctive or conjunctive normal form (KAM logic). A feature of KAM logic is a continuous dependence on input variables and a potential variety of continuous logic functions. Based on the previously proposed «fuzzy» (distributed) continuous function, in the form of a superposition of «clear» functions, and the tunable QAM element circuit that implements it, this element is generalized to a network QAM element with several tunable outputs. The multiplication functions in this QAM element can be performed using a known memristor, which can be replaced by a memtransistor based on a field effect transistor. In quantum QAM networks, these elements are, respectively: «k-memristor» and «k-memtransistor». One of the options for a k-memtransistor is a composite hybrid spin-field-effect transistor based on a planar spin valve with magnetic memory and a field-effect transistor with a ferroelectric memory. It is noted that the main technological problem of quantum QAM networks based on such a hybrid spin transistor is the creation of planar conducting and ferromagnetic elements based on ultrathin metallic and ferromagnetic films on silicon.
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Li, Zhitao, Yuqian Guo, and Weihua Gui. "Asymptotical feedback controllability of continuous-time probabilistic logic control networks." Nonlinear Analysis: Hybrid Systems 47 (February 2023): 101265. http://dx.doi.org/10.1016/j.nahs.2022.101265.

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Yadav, Neetika, Neeta Pandey, and Deva Nand. "Leakage reduction in dual mode logic through gated leakage transistors." Microprocessors and Microsystems 84 (July 2021): 104269. http://dx.doi.org/10.1016/j.micpro.2021.104269.

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Chen, Lanlan. "Secure Data Sequence Recognition of All-Optical High-Speed Network Using Semiconductor Optical Amplifier." Journal of Nanoelectronics and Optoelectronics 16, no. 10 (October 1, 2021): 1667–74. http://dx.doi.org/10.1166/jno.2021.3124.

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Semiconductor optical amplifier (SOA) has nonlinear optical effect and integration advantages, and is widely used in all-optical logic gates. Hence, it is used in the identification of all-optical high-speed network security data sequences. First, the SOA model is established and then simplified based on the existing model. On this basis, the identification model of all-optical high-speed network security data sequence is established, and the concept of integrated turbo-switch is introduced. The structure of the turbo-switch is analyzed. Then, an integrated turbo-switch architecture based on SOA-Mach Zehnder interferometer (MZI) is proposed, and its performance is verified by simulation. A data sequence recognition method for all-optical high-speed network security is proposed based on the integrated acceleration switch, and the difficulty of result recognition is analyzed. The simulation results of integrated turbo-switch show that when 1550 nm light passes through MZI, the interference can be almost completely cancelled, and the corresponding phase difference of 1538 nm light is less than 3 radians, that is, some light can pass through MZI. After the bias current of SOA of upper and lower arms of SOA-MZI is properly adjusted, MZI can play the function of tunable filter. Adjusting the bias current of SOA1 and SOA2 in the turbo-switch can control the “overshoot” program in the gain recovery curve, make SOA2 in a saturated working state, optimize the gain recovery curve, and improve the SOArelated mode effect, so that the turbo-switch can also output a more stable waveform under continuous “1” input. The recognition difficulty test shows that the target sequence after the cycle and the data sequence to be recognized are used for the “Exclusive NOR” operation, and the “AND gate” is added to realize the data sequence recognition and reduce the recognition difficulty.
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Buckley, J. J., and Yoichi Hayashi. "Numerical relationships between neural networks, continuous functions, and fuzzy systems." Fuzzy Sets and Systems 60, no. 1 (November 1993): 1–8. http://dx.doi.org/10.1016/0165-0114(93)90283-n.

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Csiszár, Orsolya, Gábor Csiszár, and József Dombi. "Interpretable neural networks based on continuous-valued logic and multicriteria decision operators." Knowledge-Based Systems 199 (July 2020): 105972. http://dx.doi.org/10.1016/j.knosys.2020.105972.

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Sturlaugson, Liessman, Logan Perreault, and John W. Sheppard. "Factored performance functions and decision making in continuous time Bayesian networks." Journal of Applied Logic 22 (July 2017): 28–45. http://dx.doi.org/10.1016/j.jal.2016.11.030.

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Dissertations / Theses on the topic "Gated Continuous Logic Networks"

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Habib, Carol. "Energy-efficient data collection and fusion in wireless body sensor networks for continuous health monitoring." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCD046.

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Plusieurs défis existent dans les réseaux de capteurs corporels sans fil tels que la collecte et la fusion de données physiologiques dans un environnement contraignant. En effet, les nœuds de capteurs sans fil ont des ressources limitées en énergie, traitement et mémoire. En outre, une grande quantité de données est collectée. Ces données sont hétérogènes, ambiguës et imprécises. Ajoutons que l'interprétation des données est influencée par plusieurs facteurs externes tels que les informations contextuelles fournies par la personne surveillée. En conséquence la prise de décisions et l'analyse des informations extraite sont influencées.Tout d'abord une technique de collecte de données est proposée. Celle-ci a pour intérêt de réduire la quantité de données collectée et la consommation d'énergie. Dans le modèle proposé, l'énergie consommée par les nœuds capteurs sans fil pour capter et pour transmettre les signes vitaux est particulièrement ciblée. Il s'agit à la fois d'un mécanisme temps-réel pour l'adaptation du taux d'échantillonnage et d'un système de détection local permettant aux nœuds de transmettre uniquement les données indiquant un changement dans l'état de santé de la personne.Deuxièmement, un modèle de fusion de données pour l'évaluation de l'état de santé de la personne surveillée est proposé. Les données fusionnées sont les signes vitaux de la personne qui proviennent de plusieurs capteurs. Ces données sont interprétées de manière humaine et sont caractérisées par l'ambiguïté et l'imprécision. Ainsi, nous proposons d'utiliser un système d'inférence floue.Ensuite, nous proposons d'évaluer l'état de santé de la personne surveillée tout en prenant en compte le contexte dans lequel elle se trouve. Étant donné que les signes vitaux de l'être humain ainsi que son contexte tels que : son activité physique, son dossier médical et ses informations personnelles sont fortement corrélés, interprétation des signes vitaux est largement influencée. Plus particulièrement, nous proposons d'utiliser les ensembles flous hésitants pour déterminer subjectivement l'intensité de l'activité physique de la personne. L'approche proposée prend en considération le profil de la personne ainsi que les caractéristiques de l'activité physique en cours.Finalement, une application médicale spécifique est ciblée. Nous proposons de détecter et d'évaluer le stress en temps réel tout en considérant la consommation d'énergie. Shimmer 3 GSR + est utilisé comme capteur sans fil pour capter le signal Photoplethysmogram (PPG) et la conductance cutanée. Une application mobile Android est développée pour extraire du signal PPG les signes vitaux qui sont corrélés au stress tels que la fréquence cardiaque, la fréquence respiratoire et la pression artérielle
Several challenges exist in Wireless Body Sensor Networks such as the data collection and fusion especially that (1) wireless sensor nodes have limited energy, processing and memory resources, (2) the amount of periodically gathered data is huge, (3) the gathered data are characterized by a heterogeneous nature and (4) the data interpretation to ensure decision-support is influenced byseveral external factors such as the provided context information of the monitored person.In this thesis, the aforementioned challenges were tackled by proposing scientific aproaches. Firstly, an energy-efficient data collection technique is proposed. This technique targets the energy consumed by biosensor nodes for sensing and transmitting vital signs. It consists of a real-timesampling rate adaptation mechanism and a local detection system which are provided at the level of the nodes. Second, in order to perform a health assessment based on the collected data, a multisensor data fusion model is proposed. In this approach, the coordinator of the network performs anassessment of the patient's health condition based on the collected measurements of his/her vital signs. Such data is interpreted in a human-reasoning way and are characterized by ambiguity and imprecision. Thus, we propose to use a Fuzzy Inference System. Then, given that vital signs are highly correlated to the context of the monitored person, a context-aware multi-sensor data fusionmodel for health assessment is proposed. The person's context include his/her physical activity status, medical record and personal information. This information highly influences the interpretation of vital signs. Hesitant fuzzy sets are used to subjectively evaluate the intensity of the person's physical activities based on his/her personal information and the activity's characteristics. Finally, a specific healthcare monitoring application is targeted. A real-time stress detection and evaluation framework is proposed while taking into consideration the energy consumption constraint. Shimmer 3 GSR+ is used as a wireless sensor node to sense the Photoplethysmogram (PPG) signal and the skin conductance. An android mobile application is developed to extract from the PPG signal stress correlated vital signs such as the heart rate, the respiration rate and the blood pressure
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Behaegel, Jonathan. "Modèles hybrides de réseaux de régulation : étude du couplage des cycles cellulaire et circadien." Thesis, Université Côte d'Azur (ComUE), 2018. http://www.theses.fr/2018AZUR4071/document.

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La modélisation de systèmes biologiques est devenue indispensable pour comprendre les phénomènes complexes et émergents issus d'influences partiellement connues, et pour envisager de contrôler un système altéré dans le but de restaurer un comportement physiologique. Tout modèle, quel que soit son paradigme sous-jacent, fait intervenir des paramètres gouvernant sa dynamique mais les mesures expérimentales ne permettent généralement pas de les identifier et cela reste l'un des problèmes majeurs de la modélisation. Cette thèse propose une méthode automatique d'identification des paramètres dynamiques de systèmes biologiques dans un cadre de modélisation hybride. Le cadre hybride choisi découpe l'espace des phases selon l'activité des entités biologiques, et associe à chacun de ces sous-espaces une vitesse d'évolution de chacun des composants. Nous proposons une logique de Hoare en temps continu ainsi qu'un calcul de plus faible précondition qui, à partir d'observations expérimentales qualitatives et chronométriques, construit les contraintes minimales sur les paramètres du modèle pour qu’il soit compatible avec les observations. Ce calcul mène à un problème de satisfaction de contraintes sur les réels et nous montrons que celui-ci peut être résolu par le solveur AbSolute.Le prototype Holmes BioNet développé au cours de cette thèse peut non seulement automatiser le processus d'identification des valeurs des paramètres à partir des observations expérimentales, mais aussi simuler l'évolution du modèle obtenu afin de le comparer avec les traces expérimentales. Nous utilisons ce prototype pour modéliser le couplage des cycles cellulaire et circadien
Modelling biological systems has become instrumental to understand complex and emerging phenomena resulting from partially known influences, and to consider controlling an altered system in order to restore a physiological behaviour. Any model, independent of the underlying paradigm, involves parameters governing its dynamics. However, experimental measurements generally do not allow their identification and this remains one of the major problems of modelling. This PhD proposes an automatic method for identifying the dynamic parameters of biological systems in a hybrid modelling framework. The chosen hybrid framework splits the phase space according to the activity of the biological entities, and associates to each of these subspaces a celerity for each of the components. We introduce a continuous time Hoare logic as well as its weakest precondition calculus which, from qualitative and chronometrical experimental observations, constructs the minimum constraints on the model parameters making it compatible with the observations. This calculus leads to a Constraint Satisfaction Problem on real numbers and we show that it can be solved by the AbSolute solver.The Holmes BioNet prototype developed during this PhD can not only automate the parameter identification process from experimental data, but also simulate the evolution of the obtained model in order to compare it with experimental traces. We use this prototype to model the coupling of the cellular and circadian cycles
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Dalecký, Štěpán. "Neuro-fuzzy systémy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236066.

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The thesis deals with artificial neural networks theory. Subsequently, fuzzy sets are being described and fuzzy logic is explained. The hybrid neuro-fuzzy system stemming from ANFIS system is designed on the basis of artificial neural networks, fuzzy sets and fuzzy logic. The upper-mentioned systems' functionality has been demonstrated on an inverted pendulum controlling problem. The three controllers have been designed for the controlling needs - the first one is on the basis of artificial neural networks, the second is a fuzzy one, and the third is based on ANFIS system.  The thesis is aimed at comparing the described systems, which the controllers have been designed on the basis of, and evaluating the hybrid neuro-fuzzy system ANFIS contribution in comparison with particular theory solutions. Finally, some experiments with the systems are demonstrated and findings are assessed.
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Raja, Ravi. "Boolean Functional Synthesis using Gated Continuous Logic Networks." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5918.

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Boolean Functional Synthesis (BFS) is a well-known challenging problem in the domain of automated program synthesis from logical specifications. This problem aims to synthesize a Boolean function that is correct-by-construction with respect to the declared specification; this specification symbolically relates the inputs and outputs of the function to be synthesized. Since Boolean functions are the basic building blocks of modern digital systems, BFS has applications in a wide range of areas, including QBF-SAT solving, circuit repair and debugging. This has motivated the community to develop practically efficient algorithms for synthesizing compact Boolean functions, which is a non-trivial endeavor. However, to the best of our knowledge, current techniques are unable to specify a bound on the Boolean function size during synthesis. Specifying a bound on the size of the formula offers flexibility in synthesizing minimal-sized Boolean functions. Learning Boolean functions from logical specifications using neural networks is a difficult problem as it requires the network to represent Boolean functions. Boolean functions are discrete functions and consequently, non-differentiable. Thus, learning a Boolean function directly using traditional neural networks is not possible. Recently Ryan et al proposed the Gated Continuous Logic Network (GCLN) model that builds on Fuzzy Logic to represent Boolean and linear integer operator, in the context of learning invariants for programs. In this work, we investigate the use of the GCLN model to synthesize solutions to the BFS problem. Our model lets us bound the number of clauses used in the synthesized Boolean function. We implement this approach in our tool BNSynth (for Bounded Neural Synthesis) which also uses sampling and counter-example-guided techniques to synthesize Boolean functions. We validate our hypothesis that this system can learn smaller functions as compared to a state-of-the-art tool over custom benchmarks and LUT-based benchmarks from the ISCAS85 benchmark suite. We observe a 15.8X average improvement in formula size by the number of clauses for these benchmarks. This empirically shows that our system can synthesize smaller Boolean functions compared to the state-of-the-art.
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Book chapters on the topic "Gated Continuous Logic Networks"

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Li, Xiaolin. "The Classical Logic and the Continuous Logic." In Lecture Notes in Networks and Systems, 511–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18461-1_33.

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Carreon-Ortiz, Hector, Fevrier Valdez, and Oscar Castillo. "A New Continuous Mycorrhiza Optimization Nature-Inspired Algorithm." In Hybrid Intelligent Systems Based on Extensions of Fuzzy Logic, Neural Networks and Metaheuristics, 147–64. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28999-6_10.

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Dombi, József, and Orsolya Csiszár. "Interpretable Neural Networks Based on Continuous-Valued Logic and Multi-criteria Decision Operators." In Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools, 147–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72280-7_9.

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García-Morales, Miguel Á., Héctor J. Fraire-Huacuja, José A. Brambila-Hernández, Juan Frausto-Solís, Laura Cruz-Reyes, Claudia G. Gómez-Santillán, and Juan M. Carpio-Valadez. "Particle Swarm Optimization Algorithm with Improved Opposition-Based Learning (IOBL-PSO) to Solve Continuous Problems." In Hybrid Intelligent Systems Based on Extensions of Fuzzy Logic, Neural Networks and Metaheuristics, 115–26. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28999-6_7.

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Bobed, Carlos, Fernando Bobillo, Sergio Ilarri, and Eduardo Mena. "Answering Continuous Description Logic Queries." In Mobile Computing and Wireless Networks, 893–938. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8751-6.ch039.

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During the last years, mobile computing has been the focus of many research efforts, due mainly to the ever-growing use of mobile devices. In this context, there is a need to manage dynamic data, such as location data or other data provided by sensors. As an example, the continuous processing of location-dependent queries has been the subject of thorough research. However, there is still a need of highly expressive ways of formulating queries, augmenting in this way the systems' answer capabilities. Regarding this issue, the modeling power of Description Logics (DLs) and the inferring capabilities of their attached reasoners could fulfill this new requirement. The main problem is that DLs are inherently oriented to model static knowledge, that is, to capture the nature of the modeled objects, but not to handle changes in the property values (which requires a full ontology reclassification), as it is common in mobile computing environments (e.g., the location is expected to vary continually). In this paper, the authors present a novel approach to process continuous queries that combines 1) the DL reasoning capabilities to deal with static knowledge, with 2) the efficient data access provided by a relational database to deal with volatile knowledge. By marking at modeling time the properties that are expected to change during the lifetime of the queries, the authors'system is able to exploit both the results of the classification process provided by a DL reasoner, and the low computational costs of a database when accessing changing data (mobile environments, semantic sensors, etc.), following a two-step continuous query processing that enables us to handle continuous DL queries efficiently. Experimental results show the feasibility of the authors' approach.
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Trappenberg, Thomas P. "Recurrent associative networks and episodic memory." In Fundamentals of Computational Neuroscience, 250–300. 3rd ed. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869364.003.0009.

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Abstract This chapter continues the discussion on the dynamics of recurrent networks. Instead of using the characteristic recurrent weight profile of the previous chapter, the networks are now trained on random patterns. It is shown that such networks can function as auto-associative memories, which can rapidly store items and are able to recall stored items from partial information. In contrast to DNF models, which have a continuous manifold of point attractors (continuous attractors for short), the models here have isolated point attractors of their dynamics. We discuss the storage capacity of point attractor networks and their extraordinary robustness to noise and lesions. But memories can also break down rapidly when overloading the network or when lesions become too severe, and we will study the physics of such sharp transitions to amnesic phases. We also review gated recurrent networks such as long short-term memory.
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Dasso, Aristides, and Ana Funes. "Threat and Risk Assessment Using Continuous Logic." In Research Anthology on Business Aspects of Cybersecurity, 156–72. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3698-1.ch007.

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Threat and Risk Assessment is an important area in cybersecurity. It covers multiple systems and organizations where cybersecurity is significant, such as banking, industry, SCADA, Energy Management System, among many others. The chapter presents a method to help assessing threats and risks associated with computer and networks systems. It integrates the Framework for Improving Critical Infrastructure Cybersecurity—developed by the National Institute of Standards and Technology—with a quantitative method based on the use of a Continuous Logic, the Logic Scoring of Preference (LSP) method. LSP is a method suitable for decision making that provides the guidelines to produce a model to assist the expert in the process of assessing how much a product or system satisfy a number of requirements, in this case associated to the identification, protection, detection, response and recovery of threat and risks in an organization.
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Dasso, Aristides, and Ana Funes. "Threat and Risk Assessment Using Continuous Logic." In Encyclopedia of Organizational Knowledge, Administration, and Technology, 1212–27. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3473-1.ch083.

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Threat and Risk Assessment is an important area in cybersecurity. It covers multiple systems and organizations where cybersecurity is significant, such as banking, industry, SCADA, Energy Management System, among many others. The chapter presents a method to help assessing threats and risks associated with computer and networks systems. It integrates the Framework for Improving Critical Infrastructure Cybersecurity—developed by the National Institute of Standards and Technology—with a quantitative method based on the use of a Continuous Logic, the Logic Scoring of Preference (LSP) method. LSP is a method suitable for decision making that provides the guidelines to produce a model to assist the expert in the process of assessing how much a product or system satisfy a number of requirements, in this case associated to the identification, protection, detection, response and recovery of threat and risks in an organization.
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Preto, Sandro, and Marcelo Finger. "Chapter 28. Effective Reasoning over Neural Networks Using Łukasiewicz Logic." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230160.

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In this chapter, we present a research line whose aim is to represent neural networks in Łukasiewicz Infinitely-valued Logic (Ł∞) so that one might reason about such neural networks by means of logical machinery. We focus on a class of neural networks with inputs and outputs in the unit interval [0,1] concentrating the study on the ones that compute piecewise linear functions, which is not a strong constraint since such functions may densely approximate any continuous function. For that, we introduce the concept of representation modulo satisfiability, that enlarges the representational power of Ł∞. We derive an algorithm for building such representations, which terminates in polynomial time as long as the input is given in a suitable format. As applications of representation modulo satisfiability, we proceed by showing how properties of neural networks, such as reachability and robustness, may be encoded in Ł∞, giving rise to formal verification techniques. Finally, we present a case study where some formal verifications are performed in real-world neural network.
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Patino, Luis, André Constantinesco, and Ernest Hirsch. "Contouring Blood Pool Myocardial Gated SPECT Images with a Sequence of Three Techniques Based on Wavelets, Neural Networks, and Fuzzy Logic." In FUZZY and NEURO-FUZZY SYSTEMS in MEDICINE, 95–136. CRC Press, 2017. http://dx.doi.org/10.1201/9780203713419-5.

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Conference papers on the topic "Gated Continuous Logic Networks"

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Yao, Jianan, Gabriel Ryan, Justin Wong, Suman Jana, and Ronghui Gu. "Learning nonlinear loop invariants with gated continuous logic networks." In PLDI '20: 41st ACM SIGPLAN International Conference on Programming Language Design and Implementation. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3385412.3385986.

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Krestinskaya, O., and A. P. James. "Approximate Probabilistic Neural Networks with Gated Threshold Logic." In 2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO). IEEE, 2018. http://dx.doi.org/10.1109/nano.2018.8626302.

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Nguyen, Van Thien, William Guicquero, and Gilles Sicard. "Histogram-Equalized Quantization for logic-gated Residual Neural Networks." In 2022 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2022. http://dx.doi.org/10.1109/iscas48785.2022.9937290.

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Heinrich, Stefan, Tayfun Alpay, and Yukie Nagai. "Learning Timescales in Gated and Adaptive Continuous Time Recurrent Neural Networks." In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020. http://dx.doi.org/10.1109/smc42975.2020.9282864.

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Gupta, Nidhi. "Clock Power Analysis of Low Power Clock Gated Arithmetic Logic Unit on Different FPGA." In 2014 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2014. http://dx.doi.org/10.1109/cicn.2014.192.

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Li, Zhitao, Yuqian Guo, and Weihua Gui. "Asymptotical Feedback Controllability of Continuous-time Probabilistic Logic Control Networks." In 2020 IEEE 16th International Conference on Control & Automation (ICCA). IEEE, 2020. http://dx.doi.org/10.1109/icca51439.2020.9264501.

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Shrivastava, Gunjan, and Shivendra Singh. "Power Optimization of Sequential Circuit Based ALU Using Gated Clock &amp; Pulse Enable Logic." In 2014 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2014. http://dx.doi.org/10.1109/cicn.2014.212.

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Zhang, Wei, Yongxiang Liu, Zhuo Wang, and Jianyong Wang. "Learning to Binarize Continuous Features for Neuro-Rule Networks." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/510.

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Abstract:
Neuro-Rule Networks (NRNs) emerge as a promising neuro-symbolic method, enjoyed by the ability to equate fully-connected neural networks with logic rules. To support learning logic rules consisting of boolean variables, converting input features into binary representations is required. Different from discrete features that could be directly transformed by one-hot encodings, continuous features need to be binarized based on some numerical intervals. Existing studies usually select the bound values of intervals based on empirical strategies (e.g., equal-width interval). However, it is not optimal since the bounds are fixed and cannot be optimized to accommodate the ultimate training target. In this paper, we propose AutoInt, an approach that automatically binarizes continuous features and enables the intervals to be optimized with NRNs in an end-to-end fashion. Specifically, AutoInt automatically selects an interval for a given continuous feature in a soft manner to enable a differentiable learning procedure of interval-related parameters. Moreover, it introduces an additional soft K-means clustering loss to make the interval centres approach the original feature value distribution, thus reducing the risk of overfitting intervals. We conduct comprehensive experiments on public datasets and demonstrate the effectiveness of AutoInt in boosting the performance of NRNs.
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Krasilenko, Vladimir G., Oleg K. Kolesnitsky, and Anatoly K. Bogukhvalsky. "Creation opportunities for optoelectronic continuous logic neural elements which are the universal circuitry macrobasis of optical neural networks." In International Conference on Holography and Correlation Optics, edited by Oleg V. Angelsky. SPIE, 1995. http://dx.doi.org/10.1117/12.226696.

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Flaminio, Tommaso, Sandro Preto, and Sara Ugolini. "Reasoning about Probability via Continuous Functions." In 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/28.

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For functional representation in an algebraizable logic we mean a representation of the algebras of formulas of the logic by means of (possibly real-valued) functions. Functional representations have shown to be a key tool for the study of non-classical logics, since they allow to regard formulas as functions and, by means of them, to approach the study of typical proof theoretical properties of the logics by means of their functional semantics. In the realm of (algebraizable) fuzzy logics, possibly the most well-known result in this respect is McNaughton theorem that shows formulas of the infinite-valued Lukasiewicz calculus to correspond, up to logical equivalence, to real valued continuous and piecewise linear functions. The functional representation for many-valued logics has been very recently shown in a paper by the second author to have an impact outside the purely logical realm and, in particular, they can be applied to study properties of artificial neural networks. In this contribution, we will provide a functional representation for the probability modal logic FP(L) that builds on Lukasiewicz calculus by adding to it a unary operator P that reads “it is probable that”. While the logic FP(L) is not algebraizable, at least not in the usual sense due to Blok and Pigozzi, we still can provide a functional representation result for its modal formulas. In order to do so, we adapt the usual universal algebraic methods to this peculiar setting, and moreover we make use of some techniques developed in a recent paper by two of the authors where a class of purely algebraic models for FP(L) based on de Finetti's coherence criterion have been introduced and studied. Our contribution will present two ways of providing a functional representation of the algebras of formulas of the modal logic FP(L): a local one, that relies on de Finetti's coherence argument; and a global one that, instead, relies on probability distributions on a finite domain.
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