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Hirosato Seki - One of the best experts on this subject based on the ideXlab platform.

  • ICRC - On a Learning Method of the SIC Fuzzy Inference Model with Consequent Fuzzy Sets
    2019 IEEE International Conference on Rebooting Computing (ICRC), 2019
    Co-Authors: Genki Ohashi, Hirosato Seki, Masahiro Inuiguchi
    Abstract:

    In the conventional fuzzy Inference Models, various learning methods have been proposed. It is generally impossible to apply the steepest descent method to fuzzy Inference Models with consequent fuzzy sets, such as Mamdani's fuzzy Inference Model because it uses min and max operations in the Inference process. Therefore, the Genetic Algorithm (GA) was useful for learning of the above Model. In addition, it has been also proposed the method for obtaining fuzzy rules of the fuzzy Inference Models unified max operation from the steepest descent method by using equivalence property. On the other hand, Single Input Connected (SIC) fuzzy Inference Model can set a fuzzy rule of 1 input 1 output, so the number of rules can be reduced drastically. In the learning method of SIC Model unified max operation with consequent fuzzy sets, GA was only applied to the Model. Therefore, this paper proposes a leaning method of SIC Model unified max operation with consequent fuzzy sets by using equivalence. Moreover, the proposed method is applied to a medical diagnosis and compared with the SIC Model by using GA.

  • iCAST - Knowledge Acquisition with Deep Fuzzy Inference Model and Its Application to a Medical Diagnosis
    2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), 2019
    Co-Authors: Yuki Mori, Hirosato Seki, Masahiro Inuiguchi
    Abstract:

    In this paper, we reduce the number of fuzzy rules in the fuzzy Inference Model and acquire knowledge as fuzzy rules. The number of input items used for the Inference Model is reduced by randomly selecting the number of input items in each layer. Therefore, it turns out that the number of rules in the whole of this Model can be reduced more than that of rules in an Inference Model that uses all the original input items at one time. However, in the previous Model by Zhang, although the consequent part of the fuzzy rule was learned, the antecedent part was not learned. Since we need to deal with the situation where there is no prior knowledge in the problem to apply and it will be necessary to acquire knowledge from data, it is required to learn the antecedent part. In this paper, we propose a learning method for the antecedent fuzzy sets in fuzzy rules in order to obtain relationship between input and output of the learning data from the actual data. Then, as an example, the proposed method is applied to medical diagnosis of diabetes, the accuracy of the previous method is compared with that of the proposed method.

  • SMC - A Learning Method of Non-Differential SIC Fuzzy Inference Model Using Genetic Algorithm and Its Application to a Medical Diagnosis
    2019 IEEE International Conference on Systems Man and Cybernetics (SMC), 2019
    Co-Authors: Genki Ohashi, Hirosato Seki, Tomoki Shimizu, Masahiro Inuiguchi
    Abstract:

    There are typical learning methods for fuzzy Inference Models such as the steepest descent method, genetic algorithm, etc. It is generally impossible to apply the steepest descent method to fuzzy Inference Models with consequent fuzzy sets, such as Mamdani’s fuzzy Inference Models that use min and max operations. Therefore, genetic algorithm will be useful for the above Model. However, the computational complexity of genetic algorithm is much larger than the steepest descent method. Also, since all input items are set to the antecedent parts in typical fuzzy Inference Model, the number of rules increases exponentially. Moreover, considering the computational complexity of genetic algorithm, it will not be necessarily suitable. On the other hand, Single Input Connected (SIC) fuzzy Inference Model sets the fuzzy rule of 1 input 1 output, so the number of rules can be reduced drastically. The consequent parts of the conventional SIC Model were real number although linguistic interpretation is possible and easy to understand if the consequent parts are fuzzy sets. Therefore, we propose a new SIC Model which extends real number of the consequent parts to fuzzy sets, and the fuzzy rules are derived by using genetic algorithm. In addition, it is applied to a medical diagnosis and compared with the conventional fuzzy Inference Models.

  • Optimization of Constrained SIRMs Connected Type Fuzzy Inference Model Using Two-Phase Simplex Method
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 2018
    Co-Authors: Takeshi Nagata, Hirosato Seki, Hiroaki Ishii
    Abstract:

    Single Input Rule Modules connected fuzzy Inference Model (SIRMs Model, for short) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy Inference Models. However, it is difficult to understand the meaning of the weight for the SIRMs Model because the value of the weight has no restriction in the learning rules. Therefore, the paper proposes a constrained SIRMs Model in which the weights are in [0,1] by using two-phase simplex method. Moreover, it shows that the applicability of the proposed Model by applying it to a medical diagnosis.

  • IUKM - On the Property of SIC Fuzzy Inference Model with Compatibility Functions
    Lecture Notes in Computer Science, 2015
    Co-Authors: Hirosato Seki
    Abstract:

    The single input connected fuzzy Inference Model (SIC Model) can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy Inference Models. However, the Inference results obtained by the SIC Model were generally simple comapred with the conventional fuzzy Inference Models. In this paper, we propose a SIC Model with compatibility functions, which weights the rules of the SIC Model. Moreover, this paper shows that the Inference results of the proposed Model can be easily obtained even as the proposed Model uses involved compatibility functions.

Min-yuan Cheng - One of the best experts on this subject based on the ideXlab platform.

  • A Self-Adaptive Fuzzy Inference Model Based on Least Squares SVM for Estimating Compressive Strength of Rubberized Concrete
    International Journal of Information Technology and Decision Making, 2016
    Co-Authors: Min-yuan Cheng, Nhat-duc Hoang
    Abstract:

    This paper presents an AI approach named as self-Adaptive fuzzy least squares support vector machines Inference Model (SFLSIM) for predicting compressive strength of rubberized concrete. The SFLSIM consists of a fuzzification process for converting crisp input data into membership grades and an Inference engine which is constructed based on least squares support vector machines (LS-SVM). Moreover, the proposed Inference Model integrates differential evolution (DE) to adaptively search for the most appropriate profiles of fuzzy membership functions (MFs) as well as the LS-SVM’s tuning parameters. In this study, 70 concrete mix samples are utilized to train and test the SFLSIM. According to experimental results, the SFLSIM can achieve a comparatively low MAPE which is less than 2%.

  • Seismic Assessment of Bridge Diagnostic in Taiwan Using the Evolutionary Support Vector Machine Inference Model ESIM
    Applied Artificial Intelligence, 2014
    Co-Authors: Min-yuan Cheng, Ruei-fu Syu
    Abstract:

    Because earthquakes cause bridge damage resulting in incidents such as traffic gridlock and casualties, it is necessary to assess possible bridge damage under different seismic intensity levels in order to reduce the incidence of disasters. However, because there are many bridges in Taiwan, the time and budget will be restricted to conduct traditional structural analysis (preliminary assessment, detailed analysis) of each bridge to obtain its yield acceleration (Ay) and collapse acceleration (Ac). Hence, this study developed an Inference Model by integrating two AI techniques: support vector machines (SVM) and fast messy genetic algorithms (fmGA). The study applied historical cases to infer Ay and Ac values by the mapping relation between preliminary assessment factors (input) of historical cases and the detailed assessment of Ay and Ac values (output). According to the above Inference Model to predict Ay and Ac values, the probability of possible bridge damage by earthquakes can be predicted as a suggest...

  • A hybrid fuzzy Inference Model based on RBFNN and artificial bee colony for predicting the uplift capacity of suction caissons
    Automation in Construction, 2014
    Co-Authors: Min-yuan Cheng, Minh-tu Cao, Duc-hoc Tran
    Abstract:

    Abstract The suction caisson is an essential part of the foundation system used in offshore platforms. The failure of a single suction caisson may cause the collapse of an entire offshore system. Hence, accurately predicting the uplift capacity of suction caissons is of critical importance to platform function and reliability. This study proposes the intelligent fuzzy radial basis function neural network Inference Model (IFRIM) to predict the uplift capacity of suction caissons. IFRIM is a hybrid of the radial basis function neural network (RBFNN), fuzzy logic (FL), and artificial bee colony (ABC) algorithm. In the IFRIM, FL deals with imprecise and uncertain information; RBFNN acts as a supervised learning technique to address fuzzy input–output mapping relationships; and ABC searches for the most appropriate parameter settings for RBFNN and FL. Comparison results show IFRIM to be the fittest Model for predicting the uplift capacity of suction caissons in terms of accuracy and reliability. A 10-fold cross-validation approach found that the IFRIM reduced the RMSE and MAPE at least 70% and 90%, respectively, below other tested Models.

  • High-performance Concrete Compressive Strength Prediction using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model
    Automation in Construction, 2012
    Co-Authors: Min-yuan Cheng, Jui-sheng Chou, Yu Wei Wu
    Abstract:

    Abstract The major different between High Performance Concrete (HPC) and conventional concrete is essentially the use of mineral and chemical admixture. These two admixtures made HPC mechanical behavior act differently compare to conventional concrete at microstructures level. Certain properties of HPC are not fully understood since the relationship between ingredients and concrete properties is highly nonlinear. Therefore, predicting HPC behavior is relatively difficult compared to predicting conventional concrete behavior. This paper proposes an Artificial Intelligence hybrid system to predict HPC compressive strength that fuses Fuzzy Logic (FL), weighted Support Vector Machines (wSVM) and fast messy genetic algorithms (fmGA) into an Evolutionary Fuzzy Support Vector Machine Inference Model for Time Series Data (EFSIMT). Validation results show that the EFSIMT achieves higher performance in comparison with Support Vector Machines (SVM) and obtains results comparable with Back-Propagation Neural Network (BPN). Hence, EFSIMT offers strong potential as a valuable predictive tool for HPC compressive strength.

  • forecasting enterprise resource planning software effort using evolutionary support vector machine Inference Model
    International Journal of Project Management, 2012
    Co-Authors: Jui-sheng Chou, Min-yuan Cheng
    Abstract:

    Abstract Despite significant advances in procedures that facilitate project management, the continued reliance of software managers on guesswork and subjective judgment causes frequent project time overruns. This study uses an Evolutionary Support Vector Machine Inference Model (ESIM) for efficiently and accurately estimating the person-hour of ERP system development projects. The proposed ESIM is a hybrid intelligence Model integrating a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). The SVM mainly provides learning and curve fitting while the fmGA minimizes errors. The analytical results in this study confirm that, compared to artificial neural networks and SVM, the proposed ESIM provides preliminary prediction at early phase of ERP software development effort for the manufacturing firms with superior accuracy, shorter training time and less overfitting. Future research can develop user-friendly expert systems with window or browser interfaces that can be used by planning personnel to flexibly input related variables and to estimate development effort and corresponding project time/cost.

Kaoru Hirota - One of the best experts on this subject based on the ideXlab platform.

  • A proposal of SIRMs dynamically connected fuzzy Inference Model for plural input fuzzy control
    Fuzzy Sets and Systems, 2002
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    Single input rule modules (SIRMs) dynamically connected fuzzy Inference Model is proposed for plural input fuzzy control. For each input item, a SIRM is constructed and a dynamic importance degree is defined. The dynamic importance degree consists of a base value insuring the role of the input item through a control process, and a dynamic value changing with control situations to adjust the dynamic importance degree. Each dynamic value can be easily tuned based on the local information of current state. The Model output is obtained by summarizing the products of the dynamic importance degree and the fuzzy Inference result of each SIRM. The controller constructing method for constant value control systems is given, and constant value controls of typical first- and second-order lag plants are tested. The simulation results show that by using the proposed mode, the reaching time can be reduced by more than 15% without any steady-state error, overshoot, or vibration compared with the SIRMs fixed importance degree connected fuzzy Inference Model. The proposed Model is further successfully applied to stabilization control of an inverted pendulum system including the position control of the cart.

  • Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 2000
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    A trajectory tracking experiment system taking an unconstrained table-tennis ball as the control object is constructed, and a fuzzy controller based on the SIRMs dynamically connected fuzzy Inference Model is proposed. For each of the three input items of the fuzzy controller, a SIRM (Single Input Rule Module) is established and an importance degree is defined. Especially for the input item corresponding to ball velocity, its importance degree is tuned dynamically according to moving conditions. The summation of the products of the importance degree and the fuzzy Inference result of the SIRMs is calculated to control the angles of a table, making the ball on the table move along a desired trajectory. A virtual spiral asymptotic trajectory is also introduced to give the object an adequate desired position at each sampling time. Tracking experiment results for three kinds of circles and one kind of ellipses show that in more than 80% of the experiments performed under the SIRMs dynamically connected fuzzy Inference Model, the maximum tracking error is smaller than 0.05m and the unevenness of the sampling steps necessary for each round is very small. Compared with conventional fuzzy controller, the SIRMs dynamically connected fuzzy Inference Model is proved to be effective in tracking control of unconstrained objects.

  • SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 1997
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    A new fuzzy Inference Model, SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model, is proposed for plural input fuzzy control. For each input item, an importance degree is defined and single input fuzzy rule module is constructed. The importance degrees control the roles of the input items in systems. The Model output is obtained by the summation of the products of the importance degree and the fuzzy Inference result of each SIRM. The proposed Model needs both very few rules and parameters, and the rules can be designed much easier. The new Model is first applied to typical secondorder lag systems. The simulation results show that the proposed Model can largely improve the control performance compared with that of the conventional fuzzy Inference Model. The tuning algorithm is then given based on the gradient descent method and used to adjust the parameters of the proposed Model for identifying 4-input 1-output nonlinear functions. The identification results indicate that the proposed Model also has the ability to identify nonlinear systems.

  • SIRMs connected fuzzy Inference Model tuning using genetic algorithm
    1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228), 1
    Co-Authors: C. Cavalcante, Kaoru Hirota
    Abstract:

    The single input rule modules (SIRMs) connected Inference Model is a fuzzy Inference Model in which a single input rule module is constructed for each system input variable. The output of the module is weighted by the degree of importance for each input and then summarized it into the system output. A tuning algorithm for this Model applied to function recognition is suggested based on the steepest descent method. However, the number of rules can not be optimized. In this work, a tuning process based on the genetic algorithm is proposed. It allows a wide search for tuned parameters with optimized number of rules. A nonlinear function recognition simulation experiment is done to confirm the validity of the proposed method.

  • SIRMs dynamically connected fuzzy Inference Model and PID controller
    1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228), 1
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    The fuzzy controller based on the single input rule modules (SIRMs) dynamically connected fuzzy Inference Model is analyzed. When the fuzzy controller uses output error and the change in the output error as input items and the change in the manipulated variable as output item to control first-order lag plants, it is proved to be a nonlinear PI controller. When it adds the second-order change in the output error to the input items for second-order lag plants, the fuzzy controller works as a nonlinear PID controller which consists of nonlinear P-action, I-action and D-action. Control experiments are also performed. The results show that the fuzzy controller can reduce the reaching time without causing any overshoot or steady state error compared with the conventional PI or PLD controller.

Mark Sammons - One of the best experts on this subject based on the ideXlab platform.

  • semantic and logical Inference Model for textual entailment
    Meeting of the Association for Computational Linguistics, 2007
    Co-Authors: Dan Roth, Mark Sammons
    Abstract:

    We compare two approaches to the problem of Textual Entailment: SLIM, a compositional approach Modeling the task based on identifying relations in the entailment pair, and BoLI, a lexical matching algorithm. SLIM's framework incorporates a range of resources that solve local entailment problems. A search-based Inference procedure unifies these resources, permitting them to interact flexibly. BoLI uses WordNet and other lexical similarity resources to detect correspondence between related words in the Hypothesis and the Text. In this paper we describe both systems in some detail and evaluate their performance on the 3rd PASCAL RTE Challenge. While the lexical method outperforms the relation-based approach, we argue that the relation-based Model offers better long-term prospects for entailment recognition.

  • an Inference Model for semantic entailment in natural language
    Lecture Notes in Computer Science, 2006
    Co-Authors: Rodrigo De Salvo Braz, Dan Roth, Roxana Girju, Vasin Punyakanok, Mark Sammons
    Abstract:

    Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. We present a principled approach to semantic entailment that builds on inducing re-representations of text snippets into a hierarchical knowledge representation along with an optimization-based inferential mechanism that makes use of it to prove semantic entailment. This paper provides details and analysis of the knowledge representation and knowledge resources issues encountered. We analyze our system's behavior on the PASCAL text collection 1 and the PARC collection of question-answer pairs 2 . This is used to motivate and explain some of the design decisions in our hierarchical knowledge representation, that is centered around a predicate-argument type abstract representation of text.

  • an Inference Model for semantic entailment in natural language
    International Joint Conference on Artificial Intelligence, 2005
    Co-Authors: Rodrigo De Salvo Braz, Dan Roth, Roxana Girju, Vasin Punyakanok, Mark Sammons
    Abstract:

    Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. This is a fundamental problem in natural language understanding that provides a broad framework for studying language variability and has a large number of applications. We present a principled approach to this problem that builds on inducing re-representations of text snippets into a hierarchical knowledge representation along with a sound inferential mechanism that makes use of it to prove semantic entailment.

  • an Inference Model for semantic entailment in natural language
    National Conference on Artificial Intelligence, 2005
    Co-Authors: Rodrigo De Salvo Braz, Dan Roth, Roxana Girju, Vasin Punyakanok, Mark Sammons
    Abstract:

    Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. This is a fundamental problem in natural language understanding that provides a broad framework for studying language variability and has a large number of applications. This paper presents a principled approach to this problem that builds on inducing representations of text snippets into a hierarchical knowledge representation along with a sound optimization-based inferential mechanism that makes use of it to decide semantic entailment. A preliminary evaluation on the PASCAL text collection is presented.

Jui-sheng Chou - One of the best experts on this subject based on the ideXlab platform.

  • High-performance Concrete Compressive Strength Prediction using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model
    Automation in Construction, 2012
    Co-Authors: Min-yuan Cheng, Jui-sheng Chou, Yu Wei Wu
    Abstract:

    Abstract The major different between High Performance Concrete (HPC) and conventional concrete is essentially the use of mineral and chemical admixture. These two admixtures made HPC mechanical behavior act differently compare to conventional concrete at microstructures level. Certain properties of HPC are not fully understood since the relationship between ingredients and concrete properties is highly nonlinear. Therefore, predicting HPC behavior is relatively difficult compared to predicting conventional concrete behavior. This paper proposes an Artificial Intelligence hybrid system to predict HPC compressive strength that fuses Fuzzy Logic (FL), weighted Support Vector Machines (wSVM) and fast messy genetic algorithms (fmGA) into an Evolutionary Fuzzy Support Vector Machine Inference Model for Time Series Data (EFSIMT). Validation results show that the EFSIMT achieves higher performance in comparison with Support Vector Machines (SVM) and obtains results comparable with Back-Propagation Neural Network (BPN). Hence, EFSIMT offers strong potential as a valuable predictive tool for HPC compressive strength.

  • forecasting enterprise resource planning software effort using evolutionary support vector machine Inference Model
    International Journal of Project Management, 2012
    Co-Authors: Jui-sheng Chou, Min-yuan Cheng
    Abstract:

    Abstract Despite significant advances in procedures that facilitate project management, the continued reliance of software managers on guesswork and subjective judgment causes frequent project time overruns. This study uses an Evolutionary Support Vector Machine Inference Model (ESIM) for efficiently and accurately estimating the person-hour of ERP system development projects. The proposed ESIM is a hybrid intelligence Model integrating a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). The SVM mainly provides learning and curve fitting while the fmGA minimizes errors. The analytical results in this study confirm that, compared to artificial neural networks and SVM, the proposed ESIM provides preliminary prediction at early phase of ERP software development effort for the manufacturing firms with superior accuracy, shorter training time and less overfitting. Future research can develop user-friendly expert systems with window or browser interfaces that can be used by planning personnel to flexibly input related variables and to estimate development effort and corresponding project time/cost.