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Journal articles on the topic 'Rule base'

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Published: 4 June 2021
Last updated: 27 April 2022

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1

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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2

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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3

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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6

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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8

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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9

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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10

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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HALAVATI, RAMIN, SAEED BAGHERI SHOURAKI, SIMA LOTFI, and POOYA ESFANDIAR. "SYMBIOTIC EVOLUTION OF RULE BASED CLASSIFIER SYSTEMS." International Journal on Artificial Intelligence Tools 18, no. 01 (February 2009): 1–16. http://dx.doi.org/10.1142/s0218213009000019.

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Evolutionary Algorithms are vastly used in development of rule based classifier systems in data mining where the rule base is usually a set of If-Then rules and an evolutionary trait develops and optimizes these rules. Genetic Algorithm is usually a favorite solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. Also, designing a good genetic algorithm for rule base evolution requires the design of a recombination operator that merges two rule bases without disrupting the functionalities of each of them. To overcome the speed problem and the need to design recombination operator, this paper presents a novel algorithm for rule base evolution based on natural process of symbiogenesis. The algorithm uses symbiotic combination operator instead of traditional sexual recombination operator of genetic algorithms. This operator takes two chromosomes with different number of genes (rules here) and merges them by combining all the information content of both chromosomes. Using this operator results in two major advantages: First, it totally removes the need to design the recombination operator and therefore is easier to use; second, it outperforms traditional genetic algorithm both in emergence speed and classification rate, this is tested and presented on some globally used benchmarks.
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12

Liu, Feng, Chai Quek, and Geok See Ng. "A Novel Generic Hebbian Ordering-Based Fuzzy Rule Base Reduction Approach to Mamdani Neuro-Fuzzy System." Neural Computation 19, no. 6 (June 2007): 1656–80. http://dx.doi.org/10.1162/neco.2007.19.6.1656.

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There are two important issues in neuro-fuzzy modeling: (1) interpretability—the ability to describe the behavior of the system in an interpretable way—and (2) accuracy—the ability to approximate the outcome of the system accurately. As these two objectives usually exert contradictory requirements on the neuro-fuzzy model, certain compromise has to be undertaken. This letter proposes a novel rule reduction algorithm, namely, Hebb rule reduction, and an iterative tuning process to balance interpretability and accuracy. The Hebb rule reduction algorithm uses Hebbian ordering, which represents the degree of coverage of the samples by the rule, as an importance measure of each rule to merge the membership functions and hence reduces the number of the rules. Similar membership functions (MFs) are merged by a specified similarity measure in an order of Hebbian importance, and the resultant equivalent rules are deleted from the rule base. The rule with a higher Hebbian importance will be retained among a set of rules. The MFs are tuned through the least mean square (LMS) algorithm to reduce the modeling error. The tuning of the MFs and the reduction of the rules proceed iteratively to achieve a balance between interpretability and accuracy. Three published data sets by Nakanishi (Nakanishi, Turksen, & Sugeno, 1993), the Pat synthetic data set (Pal, Mitra, & Mitra, 2003), and the traffic flow density prediction data set are used as benchmarks to demonstrate the effectiveness of the proposed method. Good interpretability, as well as high modeling accuracy, are derivable simultaneously and are suitably benchmarked against other well-established neuro-fuzzy models.
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13

Rajab, Sharifa. "Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling." International Journal of Fuzzy System Applications 9, no. 2 (April 2020): 31–58. http://dx.doi.org/10.4018/ijfsa.2020040102.

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Neuro-fuzzy systems based on a fuzzy model proposed by Takagi, Sugeno and Kang known as the TSK fuzzy model provide a powerful method for modelling uncertain and highly complex non-linear systems. The initial fuzzy rule base in TSK neuro-fuzzy systems is usually obtained using data driven approaches. This process induces redundancy into the system by adding redundant fuzzy rules and fuzzy sets. This increases complexity which adversely affects generalization capability and transparency of the fuzzy model being designed. In this article, the authors explore the potential of TSK fuzzy modelling in developing comparatively interpretable neuro-fuzzy systems with better generalization capability in terms of higher approximation accuracy. The approach is based on three phases, the first phase deals with automatic data driven rule base induction followed by rule base simplification phase. Rule base simplification uses similarity analysis to remove similar fuzzy sets and resulting redundant fuzzy rules from the rule base, thereby simplifying the neuro-fuzzy model. During the third phase, the parameters of membership functions are fine-tuned using a constrained hybrid learning technique. The learning process is constrained which prevents unchecked updates to the parameters so that a highly complex rule base does not emerge at the end of model optimization phase. An empirical investigation of this methodology is done by application of this approach to two well-known non-linear benchmark forecasting problems and a real-world stock price forecasting problem. The results indicate that rule base simplification using a similarity analysis effectively removes redundancy from the system which improves interpretability. The removal of redundancy also increased the generalization capability of the system measured in terms of increased forecasting accuracy. For all the three forecasting problems the proposed neuro-fuzzy system demonstrated better accuracy-interpretability tradeoff as compared to two well-known TSK neuro-fuzzy models for function approximation.
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14

Murty, M. S. N., and G. Suresh Kumar. "On Controllability and Observability of Fuzzy Dynamical Matrix Lyapunov Systems." Advances in Fuzzy Systems 2008 (2008): 1–16. http://dx.doi.org/10.1155/2008/421525.

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We provide a way to combine matrix Lyapunov systems with fuzzy rules to form a new fuzzy system called fuzzy dynamical matrix Lyapunov system, which can be regarded as a new approach to intelligent control. First, we study the controllability property of the fuzzy dynamical matrix Lyapunov system and provide a sufficient condition for its controllability with the use of fuzzy rule base. The significance of our result is that given a deterministic system and a fuzzy state with rule base, we can determine the rule base for the control. Further, we discuss the concept of observability and give a sufficient condition for the system to be observable. The advantage of our result is that we can determine the rule base for the initial value without solving the system.
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15

Bouraoui, Zied, and Steven Schockaert. "Automated Rule Base Completion as Bayesian Concept Induction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6228–35. http://dx.doi.org/10.1609/aaai.v33i01.33016228.

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Considerable attention has recently been devoted to the problem of automatically extending knowledge bases by applying some form of inductive reasoning. While the vast majority of existing work is centred around so-called knowledge graphs, in this paper we consider a setting where the input consists of a set of (existential) rules. To this end, we exploit a vector space representation of the considered concepts, which is partly induced from the rule base itself and partly from a pre-trained word embedding. Inspired by recent approaches to concept induction, we then model rule templates in this vector space embedding using Gaussian distributions. Unlike many existing approaches, we learn rules by directly exploiting regularities in the given rule base, and do not require that a database with concept and relation instances is given. As a result, our method can be applied to a wide variety of ontologies. We present experimental results that demonstrate the effectiveness of our method.
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Chen, Jin Qiang, Kai Guo, and Qun Zhan Li. "Application of Three-Ratio Fault Diagnosis Rule Based on Belief Rule Base." Advanced Materials Research 677 (March 2013): 418–22. http://dx.doi.org/10.4028/www.scientific.net/amr.677.418.

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A transformer fault diagnosis method based on RIMER( belief rule-base inference methodology using the evidential reasoning approach) expert system is proposed to deal with fault coding loss and low accuracy in the three-ratio rule. The fault diagnosis method acquires the parameter of the belief rule bases from both three-ratio rule and training of the fault sample data, and then get the diagnosis result Based on the belief rule bases and evidence reasoning algorithm. The calculation speed of the method is fast. The simulation experiment indicates that the model improves the fault diagnosis accuracy rate, furthermore the distributed confidence result indicts the mixed fault more effective.
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17

Hamzeloo, Sam, and Mansoor Zolghadri Jahromi. "Decentralized Incremental Fuzzy Reinforcement Learning for Multi-Agent Systems." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 01 (February 2020): 79–98. http://dx.doi.org/10.1142/s021848852050004x.

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We present a new incremental fuzzy reinforcement learning algorithm to find a sub-optimal policy for infinite-horizon Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs). The algorithm addresses the high computational complexity of solving large Dec-POMDPs by generating a compact fuzzy rule-base for each agent. In our method, each agent uses its own fuzzy rule-base to make the decisions. The fuzzy rules in these rule-bases are incrementally created and tuned according to experiences of the agents. Reinforcement learning is used to tune the behavior of each agent in such a way that maximum global reward is achieved. In addition, we propose a method to construct the initial rule-base for each agent using the solution of the underlying MDP. This drastically improves the performance of the algorithm in comparison with random initialization of the rule-base. We assess the performance of our proposed method using several benchmark problems in comparison with some state-of-the-art methods. Experimental results show that our algorithm achieves better or similar reward when compared with other methods. However, from the runtime point of view, our method is superior to all previous methods. Using a compact fuzzy rule-base not only decreases the amount of memory used but also significantly speeds up the learning phase.
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Schmidt, Peter Koerver. "A General Income Inclusion Rule as a Tool for Improving the International Tax Regime – Challenges Arising from EU Primary Law." Intertax 48, Issue 11 (October 1, 2020): 983–97. http://dx.doi.org/10.54648/taxi2020100.

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The overall concept of the OECD’s Global Anti-Base Erosion Proposal is to develop a coordinated set of rules to address ongoing risks from profit shifting and to curb international tax competition. Two important components of the proposal are the income inclusion rule and the switch-over rule and, in this article, these components are examined in consideration of EU primary law. Depending on the final design of the rules, it is concluded that the proposed income inclusion rule – however, probably not the switch-over rule – may end up restricting the fundamental freedoms by treating comparable situations differently. Against that background, a number of policy options for designing the income inclusion rule in accordance with primary EU law requirements are presented, and pros and cons of these design options are discussed. Global anti-base erosion proposal (GloBE), EU tax law, fundamental freedoms, tax avoidance, tax competition, tax policy
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19

Geselbracht, James J., and Douglas M. Johnston. "Issues in Rule Base Development." Journal of Water Resources Planning and Management 114, no. 4 (July 1988): 457–68. http://dx.doi.org/10.1061/(asce)0733-9496(1988)114:4(457).

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20

Carlson, Rolf, and Björn Granström. "Rule‐controlled data base search." Journal of the Acoustical Society of America 78, S1 (November 1985): S54. http://dx.doi.org/10.1121/1.2022873.

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21

Gegov, Alexander, Neelamugilan Gobalakrishnan, and David Sanders. "Filtration of Non-Monotonic Rules for Fuzzy Rule Base Compression." International Journal of Computational Intelligence Systems 7, no. 2 (October 24, 2013): 382–400. http://dx.doi.org/10.1080/18756891.2013.858904.

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22

Szilveszter Kov?cs. "Special Issue on Fuzzy Rule Interpolation." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 3 (May 20, 2011): 253. http://dx.doi.org/10.20965/jaciii.2011.p0253.

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Fuzzy Rule Interpolation (FRI) methods are well known tools for reasoning in case of insufficient knowledge expressed as sparse fuzzy rule-bases. It also provides a simple way to define fuzzy functions. Despite these advantages, FRI techniques are relatively rarely applied in practice. Enabling sparse fuzzy rule-bases, FRI dramatically simplifies rule-base creation. Regardless of whether the rule-base is generated by a human expert, or automatically from input-output data, the ability to provide reasonable interpolated conclusions even if no rule fires for a given observation, help to concentrate on cardinal actions alone. This reduces the number of rules needed, speeds up parameter optimization and validation steps, and hence simplifies rule-base creation itself. This special issuefs six papers take six different directions in current FRI research. The first introduces the FRI concept and sets up a unified criteria and evaluation system. This work collects the main properties an FRI method generally has to fulfill. The next two papers are related to the constantly important mainstream research on the more and more sophisticated FRI methods, the endeavor of finding the best way for defining a fuzzy valued fuzzy function based on data given in the form of the relation of fuzzy sets, i.e., in fuzzy rules. The second paper introduces a novel FRI method that is able to handle fuzzy observations activating multiple rule antecedents applying the concept of nonlinear fuzzy-valued function. The third paper presents a novel ganalogical-basedh FRI method that rather fits into the traditional FRI research line, improving the n-rule-based gscale and move transformationh FRI to ensure continuous approximate functions. The fourth paper addresses the issue of defining a distance function between fuzzy sets on a domain that is not necessarily Euclidean metric space. In FRI, this takes on the importance if antecedent or consequent domains are non-Euclidean metric spaces. The last two papers discuss direct FRI control applications. One is an example proving that the sparse fuzzy rule-base is an efficient knowledge representation in intelligent control solutions. The last deals with the computational efficiency of implemented FRI methods applied to direct control area, clearly showing that the sparse fuzzy rule-base is not only a convenient way for knowledge representation, but also makes FRI methods possible devices for direct embedded control applications.
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23

Brás, Glender, Alisson Marques Silva, and Elizabeth Fialho Wanner. "Multi-gene genetic programming to building up fuzzy rule-base in Neo-Fuzzy-Neuron networks." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 499–516. http://dx.doi.org/10.3233/jifs-202146.

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This paper introduces a new approach to build the rule-base on Neo-Fuzzy-Neuron (NFN) Networks. The NFN is a Neuro-Fuzzy network composed by a set of n decoupled zero-order Takagi-Sugeno models, one for each input variable, each one containing m rules. Employing Multi-Gene Genetic Programming (MG-GP) to create and adjust Gaussian membership functions and a Gradient-based method to update the network parameters, the proposed model is dubbed NFN-MG-GP. In the proposed model, each individual of MG-GP represents a complete rule-base of NFN. The rule-base is adjusted by genetic operators (Crossover, Reproduction, Mutation), and the consequent parameters are updated by a predetermined number of Gradient method epochs, every generation. The algorithm uses Elitism to ensure that the best rule-base is not lost between generations. The performance of the NFN-MG-GP is evaluated using instances of time series forecasting and non-linear system identification problems. Computational experiments and comparisons against state-of-the-art alternative models show that the proposed algorithms are efficient and competitive. Furthermore, experimental results show that it is possible to obtain models with good accuracy applying Multi-Gene Genetic Programming to construct the rule-base on NFN Networks.
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Abdalla, M. O., and T. A. Al–Jarrah. "Autogeneration of Fuzzy Logic Rule-Base Controllers." Applied Mechanics and Materials 110-116 (October 2011): 5123–30. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.5123.

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A novel Fuzzy Logic controller design methodology is presented. The method utilizes a Particle Swarm Optimization (PSO) binary search algorithm to generate the rules for the Fuzzy Logic controller rule-base stage without human experience intervention. The proposed technique is compared with the well established Lyapunov based Fuzzy Logic controller design in generating the rules. Finally, the controller’s effectiveness and performance are tested, verified and validated using an elevator control application. The novel controller’s results are to be compared with traditional Proportional Integral Derivative (PID) controller and classical Fuzzy Logic (FL) controllers.
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Grenney, William J. "A C++ Class for Rule-Base Objects." Scientific Programming 1, no. 2 (1992): 163–75. http://dx.doi.org/10.1155/1992/727852.

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A C++ class, called Tripod, was created as a tool to assist with the development of rule-base decision support systems. The Tripod class contains data structures for the rule-base and member functions for operating on the data. The rule-base is defined by three ASCII files. These files are translated by a preprocessor into a single file that is located when a rule-base object is instantiated. The Tripod class was tested as part of a proto-type decision support system (DSS) for winter highway maintenance in the Intermountain West. The DSS is composed of two principal modules: the main program, called the wrapper, and a Tripod rule-base object. The wrapper is a procedural module that interfaces with remote sensors and an external meterological database. The rule-base contains the logic for advising an inexperienced user and for assisting with the decision making process.
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Kaewkiriya, T., N. Utakrit, and M. Tiantong. "The Design of a Rule Base for an e-Learning Recommendation System Base on Multiple Intelligences." International Journal of Information and Education Technology 6, no. 3 (2016): 206–10. http://dx.doi.org/10.7763/ijiet.2016.v6.685.

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CHAKRABORTY, CHANDAN, and DEBJANI CHAKRABORTY. "FUZZY LINEAR AND POLYNOMIAL REGRESSION MODELLING OF ‘IF-THEN’ FUZZY RULEBASE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 02 (April 2008): 219–32. http://dx.doi.org/10.1142/s0218488508005145.

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In developing so called fuzzy expert systems, fuzzy rule bases have been considered with greater importance. In fact, a fuzzy rule base is a knowledgebase that models human cognitive factors. Fuzzy rules are linguistic ‘IF-THEN’ constructions where ‘IF’ part consists of a set of fuzzy variables and ‘THEN’ part includes a dependent fuzzy variable. In order to identify the underlying mathematical structure in the fuzzy rule base, we develop fuzzy linear and fuzzy polynomial regression techniques in this paper. And the estimation of model parameters is also shown using least-square approach. Finally, examples are illustrated to demonstrate the proposed model.
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Nimaiti, Maimitili, and Yamamoto Izumi. "A Rule Based Approach for Japanese-Uyghur Machine Translation System." International Journal of Software Science and Computational Intelligence 6, no. 1 (January 2014): 56–69. http://dx.doi.org/10.4018/ijssci.2014010104.

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Japanese Uyghur machine translation system has been designed and developed using recent rule based approach. Even though Japanese and Uyghur language has many similarities, but there are also some linguistic differences cause serious problems to the word for word translation. In fact, as straightforward word-for-word Japanese-Uighur translation sometimes yields unnatural Uighur sentences. To raise the translation accuracy, the authors propose a word-for-word translation system using subject verb agreement in Uighur. After a brief introduction to the comparative study of Japanese-Uyghur grammars, morphology and syntax, the authors explain their developing of a word to word rule base system. The coverage of this rule base system, the rules for translation, comparison of experimental result between statistical machine translation system and rule base machine translation system are explained. Some practical suffix translation methods solving problems in Uyghur language are also proposed.
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Guo, Hui Ling. "Research on Rule Extraction Technology Based on Genetic Algorithm in Intrusion Detection." Advanced Materials Research 760-762 (September 2013): 857–61. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.857.

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It is necessary to establish the rule base before intrusion detection. An adaptive method based on genetic algorithms was presented for learning the intrusion detection rules in order to realize the automation of attack rule generation. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment.
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STAVRAKOUDIS, DIMITRIS G., and JOHN B. THEOCHARIS. "HANDLING HIGHLY-DIMENSIONAL CLASSIFICATION TASKS WITH HIERARCHICAL GENETIC FUZZY RULE-BASED CLASSIFIERS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, supp02 (September 11, 2012): 73–104. http://dx.doi.org/10.1142/s0218488512400168.

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Many modern classification tasks are defined in highly-dimensional feature spaces. The derivation of high-performing genetic fuzzy rule-based classification systems (GFRBCSs) in such scenarios is a non-trivial task. This paper presents a framework for increasing the performance of GFRBCSs by creating a hierarchical fuzzy rule-based classifier. The proposed system is constructed through repeated invocations to a base GFRBCS procedure, considering at each step an input space fuzzy partition of a certain granularity. The best performing rules are inserted in the hierarchical rule base and the process is repeated again, considering a thicker granularity. The employed boosting scheme guides the algorithm in creating new rules to treat uncovered or misclassified patterns, thus monotonically increasing the performance of the classifier. Extensive experimental analysis in a number of real-world high-dimensional classification tasks proves the effectiveness of the proposed approach in increasing the performance of the base classifier, maintaining its interpretability to a considerable degree.
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Gailly, Frederik, and Guido L. Geerts. "Ontology-Driven Business Rule Specification." Journal of Information Systems 27, no. 1 (February 1, 2013): 79–104. http://dx.doi.org/10.2308/isys-50428.

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ABSTRACT Discovering business rules is a complex task for which many approaches have been proposed including analysis, extraction from code, and data mining. In this paper, a novel approach is presented in which business rules for an enterprise model are generated based on the semantics of a domain ontology. Starting from an enterprise model for which the business rules need to be defined, the approach consists of four steps: (1) classification of the enterprise model in terms of the domain ontology (semantic annotation), (2) matching of the enterprise model constructs with ontology-based Enterprise Model Configurations (EMCs), (3) determination of Business Rule Patterns (BRPs) associated with the EMCs, and (4) use of the semantic annotations to instantiate the business rule patterns; that is, to specify the actual business rules. The success of this approach depends on two factors: (1) the existence of a semantically rich domain ontology, and (2) the strength of the knowledge base consisting of EMC-BRP associations. The focus of this paper is on defining and illustrating the new business rule discovery approach: Ontology-Driven Business Rule Specification (ODBRS). The domain of interest is enterprise systems, and an extended version of the Resource-Event-Agent Enterprise Ontology (REA-EO) is used as the domain ontology. A small set of EMC-BRP associations—i.e., an example knowledge base—is developed for illustration purposes. The new approach is demonstrated with an example.
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Liu, Xue Bin, Xi Bin Wang, Chong Ning Li, and San Peng Deng. "The Design and Implementation of the Expert System for Cutting Parameters Based on CLIPS." Applied Mechanics and Materials 385-386 (August 2013): 731–34. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.731.

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Three core content of CLIPS expert system are researched on, namely rule base, fact base and inference engine. Using the production rules, in accordance with CLIPS syntax express the knowledge of cutting parameters, and according to the CLIPS syntax of the fact base, the rule base and the inference engine, knowledge bases of the cutting parameters expert system is built. In Visual C + + programming environment, combined with the CLIPS dynamic link library, by the interactive communication between Visual C + + and CLIPS, the software, cutting parameters expert system, has been developed successfully while has laid a good foundation for the selection of the cutting parameters to optimize.
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Jain, Sarika, Sumit Sharma, Jorrit Milan Natterbrede, and Mohamed Hamada. "Rule-Based Actionable Intelligence for Disaster Situation Management." International Journal of Knowledge and Systems Science 11, no. 3 (July 2020): 17–32. http://dx.doi.org/10.4018/ijkss.2020070102.

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Managing natural disasters is a social responsibility as they might cause a gloomy impact on human life. Efficient and timely alert systems for public and actionable recommendations for decision makers may well decrease the number of casualties. Web semantics strengthen the description of web resources for exploiting them better and making them more meaningful for both human and machine. In this work, the authors propose a semantic rule-based approach for disaster situation management (DSM) to reach the next level of decision-making power and its architecture for providing actionable intelligence in the domain of the earthquake. The system itself is based on a data pre-processing layer, a computation layer, and the middle layer relies on an extensive rule base of experts' advice stored over time and a disaster ontology along with its inherent semantics. The rule-based reasoning approach uses this knowledge base in combination with the expert rule base, written in SWRL rules, to infer recommendations for the response to an earthquake.
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Shill, Pintu Chandra, M. A. H. Akhand, MD Asaduzzaman, and Kazuyuki Murase. "Optimization of Fuzzy Logic Controllers with Rule Base Size Reduction using Genetic Algorithms." International Journal of Information Technology & Decision Making 14, no. 05 (September 2015): 1063–92. http://dx.doi.org/10.1142/s0219622015500273.

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In this paper, we present the automatic design methods with rule base size reduction for fuzzy logic controllers (FLCs) through real and binary coded coupled genetic algorithms (GAs). The adaptive schema is divided into two phases: the first phase is concerned with optimizing the FLCs membership functions and second phase called rule learning and reducing phase which automatically generates the fuzzy rules as well as determines the minimum number of rules required for building the fuzzy models. In the second phase, the redundant rules are removed by setting their all consequent weight factor to zero and merging the conflicting rules during the learning process. The first and second phases are carried out by the real and binary coded coupled GAs, respectively. Optimizing the MFs with learning and reducing rule base concurrently represents a way to maximize the performance of a FLC. The control algorithm is successfully tested for intelligent control of two degrees of freedom inverted pendulum. Finally, the simulation studies exhibits the better or competitive performance of the proposed method when compared with the existing methods.
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Last Name. "Client Device Based Content Adaptation Using Rule Base." Journal of Computer Science 7, no. 12 (December 1, 2011): 1908–13. http://dx.doi.org/10.3844/jcssp.2011.1908.1913.

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Liu, Xiaobing, Changfeng Yuan, Wei Sun, and Sen Zhang. "Product configuration system based on the rule base." International Journal of Internet and Enterprise Management 3, no. 3 (2005): 231. http://dx.doi.org/10.1504/ijiem.2005.008410.

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Gedeon, Tamás D., László T. Kóczy, and Alessandro Zorat. "Optimal Size Fuzzy Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 3 (March 20, 2007): 335–41. http://dx.doi.org/10.20965/jaciii.2007.p0335.

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Approximate models using fuzzy rule bases can be made more precise by suitably increasing the size of the rule base and decreasing uncertainty in the rules. A large rule base, however, requires more time for its evaluation and hence the problem arises of determining the size that is good enough for the task at hand, but allows as fast as possible reasoning using the rule base. This trade-off between computation time and precision is significant whenever a prediction is made which can become “out of date” or “too old” if not used in time. The trade off is considered here in the context of tracking a moving target. In this problem, a higher degree of accuracy results in tighter precision of determining target location, but at the cost of longer computation time, during which the target can move further away, thus ultimately requiring a longer search for exact target localisation. This paper examines the problem of determining the optimal rule base size that will yield a minimum total time required to repeatedly re-acquire the moving target, as done by a cat that plays with a mouse. While this problem has no known solution in its general formulation, solutions are shown here for specific contexts.
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Xing, Ya Lang, He Xin, and Jin Cheng Zhao. "Optimization Design of Fuzzy Control Rules Based on Ant Colony Algorithm." Applied Mechanics and Materials 716-717 (December 2014): 1662–65. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.1662.

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To avoid the fuzzy rules getting into “rule exploding” in fuzzy control system, a fuzzy control rules optimization algorithm based on compatibility coefficient is proposed. The method defines the compatibility coefficient of fuzzy rules, and the compatibility coefficient matrix is used to be the heuristic information in ant colony algorithm. Ant colony algorithm is used to optimize designed complete fuzzy rule base. Simulation results show that the fuzzy rules have good compatibility and control performance.
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Jin, Shangzhu. "A Bidirectional Reasoning Based on Fuzzy Interpolation." International Journal of Software Science and Computational Intelligence 12, no. 1 (January 2020): 1–14. http://dx.doi.org/10.4018/ijssci.2020010101.

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In order to deal with both the “curse of dimensionality” and the “sparse rule base” simultaneously, an initial idea of hierarchical bidirectional fuzzy interpolation is presented in this article, combining hierarchical fuzzy systems and forward/backward fuzzy rule interpolation. In particular, backward fuzzy interpolation can be employed to allow interpolation to be carried out when certain antecedents of observation variables are absent, whereas conventional methods do not work. Hierarchical bidirectional fuzzy interpolation is applicable to situations where a multiple multi-antecedent rules system needs to be reconstructed to a multi-layer fuzzy system and any sub-layer rule base is sparse. The implementation of this approach is based on fuzzy rule interpolative reasoning that utilities scale and move transformation. An illustrative example and application scenario are provided to demonstrate the efficacy of this proposed approach.
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Yamashita, Yoichi, Riichiro Mizoguchi, Osamu Kakusho, Ken'Ichi Taniguchi, and Masuzo Yanagida. "A Support System for Constructing Rule Base for Speech Synthesis by Rule. Automatic Extraction of Synthesis Rules." Systems and Computers in Japan 21, no. 4 (1990): 15–24. http://dx.doi.org/10.1002/scj.4690210402.

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CHEN, YUN, and MINORU OKADA. "STRUCTURAL ANALYSIS AND SEMANTIC UNERSTANDING FOR OFFLINE MATHEMATICAL EXPRESSIONS." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 06 (September 2001): 967–87. http://dx.doi.org/10.1142/s021800140100126x.

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As the Internet has been widely applied and the digital library developed, recognition and understanding for mathematical expressions that are included in scientific papers have become a significant research area. Here, we put forward a method to recognize and understand mathematical expressions based on a rule base and an overall design for this system. We enumerate some ambiguous problems in expression understanding based on the existing mathematical expressions found in some published books. Because understanding for the mathematical expression ambiguity has certain connections with people's sense and experience, we introduce mathematical rules, sense-based and experience-based dictionaries into the rule base. We take advantage of the parser based on mathematical rules in the rule base to understand expressions without ambiguity and utilize a combination strength function to understand expressions with ambiguity. As experimental results, layout and semantic trees for mathematical expressions are produced by making use of mathematical rules after we have decomposed two-dimensional expression structure notations into one-dimensional expressions. At the same time, some expressions with ambiguity are understood by computing and comparing combination strength among various symbols in the expressions.
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Bardamova, Marina, Anton Konev, Ilya Hodashinsky, and Alexander Shelupanov. "A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm." Symmetry 10, no. 11 (November 7, 2018): 609. http://dx.doi.org/10.3390/sym10110609.

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This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.
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Gegov, Alexander, Farzad Arabikhan, and David Sanders. "Rule base simplification in fuzzy systems by aggregation of inconsistent rules." Journal of Intelligent & Fuzzy Systems 28, no. 3 (2015): 1331–43. http://dx.doi.org/10.3233/ifs-141418.

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44

Ashrafi, Mohammad, Lloyd H. C. Chua, and Chai Quek. "The applicability of Generic Self-Evolving Takagi-Sugeno-Kang neuro-fuzzy model in modeling rainfall–runoff and river routing." Hydrology Research 50, no. 4 (March 28, 2019): 991–1001. http://dx.doi.org/10.2166/nh.2019.146.

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Abstract Recent advancements in neuro-fuzzy models (NFMs) have made possible the implementation of dynamic rule base systems. This is in comparison with static applications commonly seen in global NFMs such as the Adaptive-Network-Based Fuzzy Inference System (ANFIS) model widely used in hydrological modeling. This study underlines key differences between local and global NFMs with an emphasis on rule base dynamics, in the context of two common flow forecast applications. A global NFM, ANFIS, and two local NFMs, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK), were tested. Results from all NFMs compared favorably when benchmarked against physically based models. Rainfall–runoff modeling is a complex process which benefits from the advanced rule generation and pruning mechanisms in GSETSK, resulting in a more compact rule base. Although ANFIS resulted in the same number of rules, this came about at the expense of having the need for a large training dataset. All NFMs generated a similar number of rules for the river routing application, although local NFMs yielded better results for forecasts at longer lead times. This is attributed to the fact that the routing procedure is less complex and can be adequately modeled by static NFMs.
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Cao, Qiushi, Cecilia Zanni-Merk, Ahmed Samet, François de Bertrand de Beuvron, and Christoph Reich. "Using Rule Quality Measures for Rule Base Refinement in Knowledge-Based Predictive Maintenance Systems." Cybernetics and Systems 51, no. 2 (January 10, 2020): 161–76. http://dx.doi.org/10.1080/01969722.2019.1705550.

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46

Aminifar, Sadegh, and Arjuna bin Marzuki. "Horizontal and Vertical Rule Bases Method in Fuzzy Controllers." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/532046.

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Concept of horizontal and vertical rule bases is introduced. Using this method enables the designers to look for main behaviors of system and describes them with greater approximations. The rules which describe the system in first stage are called horizontal rule base. In the second stage, the designer modulates the obtained surface by describing needed changes on first surface for handling real behaviors of system. The rules used in the second stage are called vertical rule base. Horizontal and vertical rule bases method has a great roll in easing of extracting the optimum control surface by using too lesser rules than traditional fuzzy systems. This research involves with control of a system with high nonlinearity and in difficulty to model it with classical methods. As a case study for testing proposed method in real condition, the designed controller is applied to steaming room with uncertain data and variable parameters. A comparison between PID and traditional fuzzy counterpart and our proposed system shows that our proposed system outperforms PID and traditional fuzzy systems in point of view of number of valve switching and better surface following. The evaluations have done both with model simulation and DSP implementation.
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Muangprathub, Jirapond, Siriwan Kajornkasirat, Apirat Wanichsombat, Veera Boonjing, Jarunee Saelee, and Arthit Intarasit. "A Knowledge Integrated Case-Based Classifier." International Journal of Software Engineering and Knowledge Engineering 29, no. 06 (June 2019): 849–71. http://dx.doi.org/10.1142/s0218194019500293.

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This paper proposes a case-based classifier using a new approach that integrates rule-based and case-based reasoning approaches for enhanced accuracy. The rule-based reasoning component uses rules generated from a concept lattice of training data, binarized using fuzzy sets. These binarized data are stored as cases in the case-based classification component. The case-based component complements the rule-based component to enhance classification accuracy. Moreover, we designed the case-based component with an embedded similarity measure that uses a vector model for concept approximations. Thus, this design makes it possible to generate high quality rules and classify unseen new cases. In addition, the ability to build a knowledge base in lattice form is important for discovering hierarchical patterns, incrementing or updating the existing knowledge base, and inducing rules with our rule learning algorithm. The novel methodology was implemented and evaluated with benchmark datasets from the UCI repository and historic rubber prices in Thailand, demonstrating improvements in accuracy of classification calls. The results from the fact their several hierarchical datasets are very promising, with improved classification performance over prior reported methods.
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Wang, Ying-Ming, Long-Hao Yang, Yang-Geng Fu, Lei-Lei Chang, and Kwai-Sang Chin. "Dynamic rule adjustment approach for optimizing belief rule-base expert system." Knowledge-Based Systems 96 (March 2016): 40–60. http://dx.doi.org/10.1016/j.knosys.2016.01.003.

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Niittymäki, Jarkko. "General fuzzy rule base for isolated traffic signal control‐rule formulation." Transportation Planning and Technology 24, no. 3 (February 2001): 227–47. http://dx.doi.org/10.1080/03081060108717669.

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Wardani, Ni Wayan, and Putu Gede Surya Cipta Nugraha. "STEMMING DOKUMEN TEKS BAHASA BALI DENGAN METODE RULE BASE APPROACH." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 7, no. 3 (December 18, 2020): 510–21. http://dx.doi.org/10.35957/jatisi.v7i3.538.

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The Balinese are one of the ethnic groups of Indonesia, the majority of which are on the island of Bali, the language used is Balinese with three levels of sor-singgih (tigang soroh) guidelines, namely Basa Kasar, Basa Madia and Basa Alus. Balinese language also has the additions of pangater, seselan and pangiring. To facilitate the search for basic words in Balinese, a stemming process is needed. Stemming is the process of mapping and decomposing the form of a word into its basic form. The stemming process is very important in the information retrieval system process. In this study, the Balinese stemming process used the Rule Base Approach method. The data used in this study are 376 basic words in Balinese. This study aims to design an appropriate stemming application for Balinese stemming. The initial stage in the Balinese stemming process is to carry out the input process, preprocessing, filtering, case folding and tokenization. Each word is subjected to a stemming process to remove the additions of pangater, seselan, and pangiring. The results of the study indicate that the Rule Base Approach method can be used to stem Balinese texts, this can be seen from the results of the accuracy reaching 77.82%. Of course, in testing there are still failures caused by overstemming errors resulting from the stemming process.
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