Relevant bibliographies by topics / Rule base

Academic literature on the topic 'Rule base'

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Published: 4 June 2021
Last updated: 27 April 2022

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Journal articles on the topic "Rule base"

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Chen, Long, Jia Hua Liu, Qi Wang, Hua Sheng, and Yu Chen. "Design and Implement of Operational Rule Base Based on Machine Learning and Association Rule Mining." Applied Mechanics and Materials 734 (February 2015): 422–27. http://dx.doi.org/10.4028/www.scientific.net/amm.734.422.

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In order to ensure the security, stability and effective operation of information system, the construction and optimization techniques for information operational Rule Base has become an urgent problem to be solved. To meet the demands, this paper presents a rule base construction and optimization strategy based on machine learning and association rule mining. The operational rule base which includes basic rules, association rules and extension rules is generated by the network topology, the monitoring indicators and the association rule mining of historical data. Then implement machine learning method for rules to improve their performance. At last, the rule-upgrade strategy is proposed for rules to move from the lower region to higher region. Based on these steps, experimental results are given to verify the proposed strategy.
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Prentzas, Jim, and Ioannis Hatzilygeroudis. "Rule-based update methods for a hybrid rule base." Data & Knowledge Engineering 55, no. 2 (November 2005): 103–28. http://dx.doi.org/10.1016/j.datak.2005.02.001.

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Yudo Untoro, F. X. Wisnu. "Rules-Based System for Writing Arabic Numerals in Indonesian Words." International journal of electrical and computer engineering systems 12, no. 4 (November 26, 2021): 177–85. http://dx.doi.org/10.32985/ijeces.12.4.1.

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One of the algorithms stored in natural intelligence is the writing of Arabic numerals in Indonesian words. Algorithms in naturals intelligence are not easy to find. This problem gave us an idea to create artificial intelligence that tries to mimic natural intelligence algorithms. The proposed algorithm for building artificial intelligence is an R-Z rule-based system. This rule-based system contains a knowledge base of R-Z rules and a knowledge base of facts. In the knowledge base, the R-Z rule provides the R rule and the Z rule, while the facts knowledge base provides facts in the form of a definite standard number and an affix word. R-Z rule-based system for reasoning writing Arabic numerals in Indonesian words uses forward chaining. Artificial intelligence designs that mimic naturals intelligence in writing numbers in Indonesian words were made in C using Borland C++ 5.02 software. The experimental results show that by applying the R's rule of seven rules and Z's of twenty-five rules, the R-Z rule-based system can write Arabic numerals in Indonesian words from Arabic numerals "0" to Arabic numerals "9999999". For example, to write the Arabic number "10" in Indonesian words, the R-Z rule-based system starts with the R2 rule. Rule R2 takes action on Z3 to create new facts about Arabic numerals in the Indonesian word, namely "SEPULUH."
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Ferreiro García, R., X. Pardo Martinez, and J. Vidal Paz. "Alternative to Adjust the Rule Base on Rule Based Controllers." IFAC Proceedings Volumes 33, no. 4 (April 2000): 189–92. http://dx.doi.org/10.1016/s1474-6670(17)38242-3.

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Tan, Yao, Hubert P. H. Shum, Fei Chao, V. Vijayakumar, and Longzhi Yang. "Curvature-based sparse rule base generation for fuzzy rule interpolation." Journal of Intelligent & Fuzzy Systems 36, no. 5 (May 14, 2019): 4201–14. http://dx.doi.org/10.3233/jifs-169978.

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Boujelben, Abir, and Ikram Amous. "A method to assist in the proper management of rule bases in Web information systems." International Journal of Web Information Systems 15, no. 5 (December 2, 2019): 577–93. http://dx.doi.org/10.1108/ijwis-11-2018-0081.

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Purpose One key issue of maintaining Web information systems is to guarantee the consistency of their knowledge base, in particular, the rules governing them. There are currently few methods that can ensure that rule bases management can scale to the amount of knowledge in these systems environment. Design/methodology/approach In this paper, the authors propose a method to detect correct dependencies between rules. This work represents a preliminary step for a proposal to eliminate rule base anomalies. The authors previously developed a method that aimed to ameliorate the extraction of rules dependency relationships using a new technique. In this paper, they extend the proposal with other techniques to increase the number of extracted rules dependency relationships. The authors also add some modules to filter and represent them. Findings The authors evaluated their own method against other semantic methods. The results show that this work succeeded in extracting better numbers of correct rules dependency relationships. They also noticed that the rule groups deduced from this method’s results are very close to those provided by the rule bases developers. Originality/value This work can be applied to knowledge bases that include a fact base and a rule base. In addition, it is independent of the field of application.
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BARGHOUTI, NASER S., and GAIL E. KAISER. "SCALING UP RULE-BASED SOFTWARE DEVELOPMENT ENVIRONMENTS." International Journal of Software Engineering and Knowledge Engineering 02, no. 01 (March 1992): 59–78. http://dx.doi.org/10.1142/s021819409200004x.

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Rule-based software development environments (RBDEs) model the software development process in terms of rules that encapsulate development activities, and assist in executing the process via forward and backward chaining over the rule base. We investigate the scaling up of RBDEs to support (1) multiple views of the rule base for multiple users and (2) evolution of the rule base over the lifetime of a project. Our approach is based on clarifying two distinct functions of rules and chaining: maintaining consistency and automation. By definition, consistency is mandatory whereas automation is not. Distinguishing the consistency and automation aspects of RBDE assistance mechanisms makes it possible to formalize the range of compatible views and the scope of mechanizable evolution steps. Throughout the paper, we use the MARVEL RBDE as an example application.
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Zhu, L., Y. X. La, R. M. Shi, and S. Peng. "REMOVING LAND COVER SPURIOUS CHANGE BY GEO-ECO ZONING RULE BASE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 677–83. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-677-2020.

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Abstract. The speed of change in land cover is growing faster with the development of social science and technology. Remote sensing has become the most effective way to monitor change information. However, remote sensing images reflect only the instantaneous state of the Earth’s surface. Spectral characteristics cannot correctly reflect the actual state, and this inability results in the limited classification accuracy of land cover products. In order to obtain high accuracy change detection results, it is necessary to identify and eliminate spurious changes.At present, the spurious changes are generally identified by visual interpretation which not only labor and time consuming, but also easily lead to misjudgment due to the lack of identification experience of the interpreter. Therefore, it is urgent to establish a spurious change rule base to automatically identify spurious changes. In this study, the global geo-eco zoning can be used to build a rule base to identify and eliminate spurious changes.The structure and content of the rule base are designed, the rules are represented and put into the rule library, the plugins are designed to remove spurious changes, and a rule base management system is established to identify the spurious changes using the rules in the rule base. 30m Land cover products of Laos were selected as the experimental area to verify the accuracy of the change patches after eliminating spurious changes. Results show that the accuracy of change detection is improved by using the rule base of geo-eco zoning to identify spurious changes.
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Gegov, Alexander, David Sanders, and Boriana Vatchova. "Aggregation of inconsistent rules for fuzzy rule base simplification." International Journal of Knowledge-based and Intelligent Engineering Systems 21, no. 3 (August 9, 2017): 135–45. http://dx.doi.org/10.3233/kes-170358.

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Zyada, Zakarya, Yasuhisa Hasegawa, Gancho Vachkov, and Toshio Fukuda. "Implementing Fuzzy Learning Algorithms in a 6 DOF Hydraulic Parallel Link Manipulator: Actuators' Fuzzy Modeling." Journal of Robotics and Mechatronics 14, no. 4 (August 20, 2002): 408–19. http://dx.doi.org/10.20965/jrm.2002.p0408.

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A fuzzy-logic-based model, suitable for force control, for each hydraulic actuator of a parallel link manipulator is presented. Constructing the fuzzy model rule base mainly consists of 2 stages: (1) learning rules from examples for the known acquired input/output data of the hydraulic actuators and (2) completing unknown fuzzy rules from heuristics and experience based on the logic of actuators' behavior. We first present the algorithm of fuzzy-rule base modeling and its application for one actuator. We then present fuzzy rule base results characterizing each hydraulic actuator, differing from one to another, of a 6 DOF parallel link manipulator. Simulation output results from fuzzy models show good agreement with experimental results.
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Dissertations / Theses on the topic "Rule base"

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Hridoy, Md Rafiul Sabbir. "An Intelligent Flood Risk Assessment System using Belief Rule Base." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-65390.

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Natural disasters disrupt our daily life and cause many sufferings. Among the various natural disasters, flood is one of the most catastrophic. Assessing flood risk helps to take necessary precautions and can save human lives. The assessment of risk involves various factors which can not be measured with hundred percent certainty. Therefore, the present methods of flood risk assessment can not assess the risk of flooding accurately.  This research rigorously investigates various types of uncertainties associated with the flood risk factors. In addition, a comprehensive study of the present flood risk assessment approaches has been conducted. Belief Rule Base expert systems are widely used to handle various of types of uncertainties. Therefore, this research considers BRBES’s approach to develop an expert system to assess the risk of flooding. In addition, to facilitate the learning procedures of BRBES, an optimal learning algorithm has been proposed. The developed BRBES has been applied taking real world case study area, located at Cox’s Bazar, Bangladesh. The training data has been collected from the case study area to obtain the trained BRB and to develop the optimal learning model. The BRBES can generate different "What-If" scenarios which enables the analysis of flood risk of an area from various perspectives which makes the system robust and sustainable. This system is said to be intelligent as it has knowledge base, inference engine as well as the learning capability.
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Antoine, Emilien. "Distributed data management with a declarative rule-based language webdamlog." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00933808.

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Our goal is to enable aWeb user to easily specify distributed data managementtasks in place, i.e. without centralizing the data to a single provider. Oursystem is therefore not a replacement for Facebook, or any centralized system,but an alternative that allows users to launch their own peers on their machinesprocessing their own local personal data, and possibly collaborating with Webservices.We introduce Webdamlog, a datalog-style language for managing distributeddata and knowledge. The language extends datalog in a numberof ways, notably with a novel feature, namely delegation, allowing peersto exchange not only facts but also rules. We present a user study thatdemonstrates the usability of the language. We describe a Webdamlog enginethat extends a distributed datalog engine, namely Bud, with the supportof delegation and of a number of other novelties of Webdamlog such as thepossibility to have variables denoting peers or relations. We mention noveloptimization techniques, notably one based on the provenance of facts andrules. We exhibit experiments that demonstrate that the rich features ofWebdamlog can be supported at reasonable cost and that the engine scales tolarge volumes of data. Finally, we discuss the implementation of a Webdamlogpeer system that provides an environment for the engine. In particular, a peersupports wrappers to exchange Webdamlog data with non-Webdamlog peers.We illustrate these peers by presenting a picture management applicationthat we used for demonstration purposes.
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Wennerholm, Pia. "The Role of High-Level Reasoning and Rule-Based Representations in the Inverse Base-Rate Effect." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Universitetsbiblioteket [distributör], 2001. http://publications.uu.se/theses/91-554-5178-0/.

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Kong, Guilan. "An online belief rule-based group clinical decision support system." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/an-online-belief-rulebased-group-clinical-decision-support-system(c31a65c7-60c3-4e7a-b18e-44fee95f7da1).html.

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Around ten percent of patients admitted to National Health Service (NHS) hospitals have experienced a patient safety incident, and an important reason for the high rate of patient safety incidents is medical errors. Research shows that appropriate increase in the use of clinical decision support systems (CDSSs) could help to reduce medical errors and result in substantial improvement in patient safety. However several barriers continue to impede the effective implementation of CDSSs in clinical settings, among which representation of and reasoning about medical knowledge particularly under uncertainty are areas that require refined methodologies and techniques. Particularly, the knowledge base in a CDSS needs to be updated automatically based on accumulated clinical cases to provide evidence-based clinical decision support. In the research, we employed the recently developed belief Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) for design and development of an online belief rule-based group CDSS prototype. In the system, belief rule base (BRB) was used to model uncertain clinical domain knowledge, the evidential reasoning (ER) approach was employed to build inference engine, a BRB training module was developed for learning the BRB through accumulated clinical cases, and an online discussion forum together with an ER-based group preferences aggregation tool were developed for providing online clinical group decision support.We used a set of simulated patients in cardiac chest pain provided by our research collaborators in Manchester Royal Infirmary to validate the developed online belief rule-based CDSS prototype. The results show that the prototype can provide reliable diagnosis recommendations and the diagnostic performance of the system can be improved significantly after training BRB using accumulated clinical cases.
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Jacobs, Robert Alan Steiner John Phillip. "Improvements to autonomous forces through the use of genetic algorithms and rule base enhancement /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA275033.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, September 1993.
Thesis advisor(s): Hemant K. Bhargava ; B. Ramesh. "September 1993." Bibliography: p. 80-83. Also available online.
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Jacobs, Robert Alan, and John Phillip Steiner. "Improvements to autonomous forces through the use of genetic algorithms and rule base enhancement." Thesis, Monterey, California. Naval Postgraduate School, 1993. http://hdl.handle.net/10945/39954.

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Approved for public release; distribution is unlimited.
This thesis discusses two approaches to enhancing the performance of intelligent autonomous agents in a computer combat simulation environment so that their performances more closely model the tactical decisions made by human players. The first approach a
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Vafaie, Haleh Carleton University Dissertation Engineering Electrical. "An inferencing procedure for guaranteeing the search time of a production-rule knowledge base." Ottawa, 1986.

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Sowan, Bilal I. "Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.
Applied Science University (ASU) of Jordan
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Sowan, Bilal Ibrahim. "Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.
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Valenta, Jan. "Automatické ladění vah pravidlových bází znalostí." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-233507.

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This dissertation thesis introduces new methods of automated knowledge-base creation and tuning in information and expert systems. The thesis is divided in the two following parts. The first part is focused on the legacy expert system NPS32 developed at the Faculty of Electrical Engineering and Communication, Brno University of Technology. The mathematical base of the system is expression of the rule uncertainty using two values. Thus, it extends information capability of the knowledge-base by values of the absence of the information and conflict in the knowledge-base. The expert system has been supplemented by a learning algorithm. The learning algorithm sets weights of the rules in the knowledge base using differential evolution algorithm. It uses patterns acquired from an expert. The learning algorithm is only one-layer knowledge-bases limited. The thesis shows a formal proof that the mathematical base of the NPS32 expert system can not be used for gradient tuning of the weights in the multilayer knowledge-bases. The second part is focused on multilayer knowledge-base learning algorithm. The knowledge-base is based on a specific model of the rule with uncertainty factors. Uncertainty factors of the rule represents information impact ratio. Using a learning algorithm adjusting weights of every single rule in the knowledge base structure, the modified back propagation algorithm is used. The back propagation algorithm is modified for the given knowledge-base structure and rule model. For the purpose of testing and verifying the learning algorithm for knowledge-base tuning, the expert system RESLA has been developed in C#. With this expert system, the knowledge-base from medicine field, was created. The aim of this knowledge base is verify learning ability for complex knowledge-bases. The knowledge base represents heart malfunction diagnostic base on the acquired ECG (electrocardiogram) parameters. For the purpose of the comparison with already existing knowledge-basis, created by the expert and knowledge engineer, the expert system was compared with professionally designed knowledge-base from the field of agriculture. The knowledge-base represents system for suitable cultivar of winter wheat planting decision support. The presented algorithms speed up knowledge-base creation while keeping all advantages, which arise from using rules. Contrary to the existing solution based on neural network, the presented algorithms for knowledge-base weights tuning are faster and more simple, because it does not need rule extraction from another type of the knowledge representation.
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Books on the topic "Rule base"

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McCallum, Bennett T. Could a monetary base rule have prevented the Great Depression? Cambridge, MA: National Bureau of Economic Research, 1989.

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Carlin, Samuel Joseph. A fuzzy logic rule base in the Java programming language. [s.l: The Author], 1998.

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Gegov, Alexander. Fuzzy Networks for Complex Systems: A Modular Rule Base Approach. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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Sautel, Jacques-Hubert. Répertoire de réglures dans les manuscrits grecs sur parchemin: Base de données. Turnhout: Brepols, 1995.

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Rule of law for human rights in the ASEAN region: A base-line study. Jakarta: Human Rights Resource Centre, 2011.

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Denneheuvel, Sieger van. Constraint solving on data base systems: Design and implementation of the rule language RL/1. [Netherlands: s.n., 1991.

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Kowalski, Thaddeus J. Rule-Based Programming. Boston, MA: Springer US, 1996.

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Kowalski, Thaddeus J., and Leon S. Levy, eds. Rule-Based Programming. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1435-6.

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Kowalski, Thaddeus J. Rule-based programming. Boston: Kluwer Academic Publishers, 1996.

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Protection, United States Federal Trade Commission Bureau of Consumer. Complying with the FTC's appliance labeling rule: Requirements for lighting products : general service incandescent lamps (reflector and nonreflector) ; medium screw base compact fluorescent lamps ; general service fluorescent lamps. Washington, D.C: The Bureau, 1996.

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Book chapters on the topic "Rule base"

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Sudkamp, Thomas, Aaron Knapp, and Jon Knapp. "Effect of Rule Representation in Rule Base Reduction." In Interpretability Issues in Fuzzy Modeling, 303–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-37057-4_13.

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Khattak, A. M., Z. Pervez, W. A. Khan, S. Y. Lee, and Y. K. Lee. "A Self Evolutionary Rule-Base." In U- and E-Service, Science and Technology, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27210-3_1.

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Botoeva, Elena. "Description Logic Knowledge Base Exchange." In Web Reasoning and Rule Systems, 266–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33203-6_30.

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Vincze, Dávid, and Szilveszter Kovács. "Incremental Rule Base Creation with Fuzzy Rule Interpolation-Based Q-Learning." In Computational Intelligence in Engineering, 191–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15220-7_16.

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Jin, Shangzhu, Qiang Shen, and Jun Peng. "Hierarchical Bidirectional Fuzzy Rule Interpolation and Rule Base Refinement." In Backward Fuzzy Rule Interpolation, 107–19. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1654-8_6.

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Sudkamp, Thomas, and Robert J. Hammell. "Rule Base Completion in Fuzzy Models." In International Series in Intelligent Technologies, 313–30. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1365-6_14.

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Terziyska, Margarita, and Yancho Todorov. "Reduced Rule-Base Fuzzy-Neural Networks." In Advanced Computing in Industrial Mathematics, 199–214. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49544-6_17.

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Vlahavas, Ioannis, and Nick Bassiliades. "Integration of Multiple Rule Types." In Parallel, Object-Oriented, and Active Knowledge Base Systems, 27–35. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-6134-4_3.

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Peña, Alejandro, Humberto Sossa, and Francisco Gutierrez. "Ontology Agent Based Rule Base Fuzzy Cognitive Maps." In Agent and Multi-Agent Systems: Technologies and Applications, 328–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72830-6_34.

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Köhler, Agnes, Jean Krivine, and Jakob Vidmar. "A Rule-Based Model of Base Excision Repair." In Computational Methods in Systems Biology, 173–95. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12982-2_13.

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Conference papers on the topic "Rule base"

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Guan, Yu. "Regularization Method for Rule Reduction in Belief Rule-based SystemRegularization Method for Rule Reduction in Belief Rule-based System." In 8th International Conference on Computational Science and Engineering (CSE 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101705.

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Abstract:
Belief rule-based inference system introduces a belief distribution structure into the conventional rule-based system, which can effectively synthesize incomplete and fuzzy information. In order to optimize reasoning efficiency and reduce redundant rules, this paper proposes a rule reduction method based on regularization. This method controls the distribution of rules by setting corresponding regularization penalties in different learning steps and reduces redundant rules. This paper first proposes the use of the Gaussian membership function to optimize the structure and activation process of the belief rule base, and the corresponding regularization penalty construction method. Then, a step-by-step training method is used to set a different objective function for each step to control the distribution of belief rules, and a reduction threshold is set according to the distribution information of the belief rule base to perform rule reduction. Two experiments will be conducted based on the synthetic classification data set and the benchmark classification data set to verify the performance of the reduced belief rule base.
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Zhou, Mou, Changjing Shang, Guobin Li, Shangzhu Jin, Jun Peng, and Qiang Shen. "Fuzzy Rule Interpolation with a Transformed Rule Base." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494591.

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Khosravi R, Hossein, M. H. Yaghmaee Moghaddam, Amirhossein Shahroudi, and Hadi Sadoghi Yazdi. "FCM-fuzzy rule base: A new rule extraction mechanism." In 2011 International Conference on Innovations in Information Technology (IIT). IEEE, 2011. http://dx.doi.org/10.1109/innovations.2011.5893829.

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Tan, Yao, Jie Li, Martin Wonders, Fei Chao, Hubert P. H. Shum, and Longzhi Yang. "Towards sparse rule base generation for fuzzy rule interpolation." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737675.

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Wu, Baibing, Jian Huang, Wanying Gao, and Jiangtao Kong. "Rule Reduction in Air Combat Belief Rule Base Based on Fuzzy-Rough Set." In 2016 3rd International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2016. http://dx.doi.org/10.1109/icisce.2016.132.

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Harandi, Mehdi T., Thierry Schang, and Seth Cohen. "Rule base management using meta knowledge." In the 1986 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/16894.16880.

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Liu, Jun, Luis Martinez, and Ying-Ming Wang. "Extended belief rule base inference methodology." In 2008 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE 2008). IEEE, 2008. http://dx.doi.org/10.1109/iske.2008.4731154.

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Delgado, A. "Rule base evaluation using DNA chips." In Proceedings of 2002 American Control Conference. IEEE, 2002. http://dx.doi.org/10.1109/acc.2002.1025290.

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Du Zhang. "Fixpoint semantics for rule-base anomalies." In Fourth IEEE Conference on Cognitive Informatics, 2005. (ICCI 2005). IEEE, 2005. http://dx.doi.org/10.1109/coginf.2005.1532610.

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Wang, Shu-Xi, and Zeng-Yan Xia. "The integrative index algorithm based on fact-base and rule-base." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569073.

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Reports on the topic "Rule base"

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Noyes, James L. Expert System Rule-Base Evaluation Using Real-Time Parallel Processing. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada273701.

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McCallum, Bennett. Could A Monetary Base Rule Have Prevented the Great Depression? Cambridge, MA: National Bureau of Economic Research, November 1989. http://dx.doi.org/10.3386/w3162.

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Morgan, Charles, and Lee Moyer. Knowledge Base Applications to Adaptive Space-Time Processing, Volume 5: Knowledge-Based Tracker Rule Book. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada388902.

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Schuman, Harvey. Knowledge Base Applications to Adaptive Space-Time Processing, Volume 3: Radar Filtering Rule Book. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada388865.

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Scott, Paul T. Compliance with California Rule 480, Chrome Plating Facility, Building 243G, McClellan Air Force Base California. Fort Belvoir, VA: Defense Technical Information Center, December 1991. http://dx.doi.org/10.21236/ada249094.

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Yen, S. C. M., and P. T. C. Chu. Characterization of coal particles using rule base on-line image analysis. Technical report, September 1--November 30, 1993. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10133889.

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Yen, S. C. M., and T. C. Chu. Characterization of coal particles using rule base on-line image analysis. Technical report, March 1, 1994--May 31, 1994. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/10182840.

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Yen, S. C. M., and T. C. Chu. Characterization of coal particles using rule base on-line image analysis. [Quarterly] technical report, December 1, 1993--February 28, 1994. Office of Scientific and Technical Information (OSTI), June 1994. http://dx.doi.org/10.2172/10154716.

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Yen, S. C. M., and T. C. Chu. Characterization of coal particles using rule base on-line image analysis. Final technical report, September 1, 1993--August 31, 1994. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/10196333.

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Dyer, Rosemary M., and Gerald L. Freeman. Rule-Based Systems for Visibility Forecasts. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada214622.

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