Jyh-Ming Chen

National Taiwan University of Science and Technology, T’ai-pei, Taipei, Taiwan

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Publications (9)6.19 Total impact

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    Hahn-Ming Lee, Jyh-Ming Chen, Chun-Lin Liu
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    ABSTRACT: In this paper fuzzy rule inconsistency resolution and fuzzy rule insertion methods are proposed for fuzzy neural networks. Necessity support and possibility support (re- ferred to as support pair) are applied to detect and remove inconsistencies. In addition to the support pair, the concept of initial learning point is used to handle rule insertion. We demonstrate the use of the proposed methods in an example called the Knowledge Base Evaluator (KBE). After inconsistency resolution operations, learning is improved. Moreover, a new fuzzy rule is generated by setting initial learning point based on deleted conflict rule. The result of using rule insertion is much better than with inconsistency resolution alone.
    J. Inf. Sci. Eng. 01/2002; 18:187-210.
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    ABSTRACT: This paper presents an efficient fuzzy classifier with the ability of feature selection based on a fuzzy entropy measure. Fuzzy entropy is employed to evaluate the information of pattern distribution in the pattern space. With this information, we can partition the pattern space into nonoverlapping decision regions for pattern classification. Since the decision regions do not overlap, both the complexity and computational load of the classifier are reduced and thus the training time and classification time are extremely short. Although the decision regions are partitioned into nonoverlapping subspaces, we can achieve good classification performance since the decision regions can be correctly determined via our proposed fuzzy entropy measure. In addition, we also investigate the use of fuzzy entropy to select relevant features. The feature selection procedure not only reduces the dimensionality of a problem but also discards noise-corrupted, redundant and unimportant features. Finally, we apply the proposed classifier to the Iris database and Wisconsin breast cancer database to evaluate the classification performance. Both of the results show that the proposed classifier can work well for the pattern classification application
    IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 07/2001; · 3.78 Impact Factor
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    IEEE Transactions on Systems, Man, and Cybernetics, Part B. 01/2001; 31:426-432.
  • Hahn-Ming Lee, Jyh-Ming Chen, En-Chieh Chang
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    ABSTRACT: Knowledge-Based Neural Network with Trapezoidal Fuzzy Set (KBNN/TFS) is a fuzzy neural network model, which handles trapezoidal fuzzy inputs with the abilities of fuzzy rule revision, verification and generation. Based on KBNN/TFS, an efficiency validation method is proposed to evaluate the rule inference complexity on KBNN/TFS. Besides, three methods that simplify the structure of this fuzzy rule-based neural network model are provided to enhance the inference efficiency. Fuzzy tabulation method, the first method, is performed to do rule combination by modeling the antecedents of some specific rules and then to eliminate the don't care variables in the rules. The second method, named transitive fuzzy rule compacting method, combines the rules with the transitive relations to decrease the computational load of inference. The third method, called identical antecedent unifying method, simplifies the redundant antecedents of rules by replacing the identical antecedents of the rules with a single specific antecedent. By these methods, the structure of rules can be simplified without changing the results of its inference. The proposed efficiency validation method is used to analyze and support the results of performing these three efficiency enhancing methods. Also the simulation results show that the efficiency is enhanced after performing these three efficiency enhancing methods
    Tools with Artificial Intelligence, 1998. Proceedings. Tenth IEEE International Conference on; 12/1998
  • Hahn-Ming Lee, Chin-Chou Lin, Jyh-Ming Chen
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    ABSTRACT: In this paper, a method of character preclassification for handwritten Chinese character recognition is proposed. Since the number of Chinese characters is very large (at least 5401s for daily use), we employ two stages to reduce the candidates of an input character. In stage I, we extract the first set of primitive features from handwritten Chinese characters and use fuzzy rules to create four preclassification groups. The purpose in stage I is to reduce the candidates roughly. In stage II, we extract the second set of primitive features from handwritten Chinese characters and then use the Supervised Extended ART (SEART) as the classifier to generate preclassification classes for each preclassification group created in stage I. Since the number of characters in each preclassification class is smaller than that in the whole character set, the problem becomes simpler. In order to evaluate the proposed preclassification system, we use 605 Chinese character categories in the textbooks of elementary school as our training and testing data. The database used is HCCRBASE (provided by CCL, ITRI, Taiwan). In samples 1–100, we select the even samples as the training set, and the odd samples as the testing set. The characters of the testing set can be distributed into correct preclassification classes at a rate of 98.11%.
    International Journal of Pattern Recognition and Artificial Intelligence 09/1998; 12:743-761. · 0.56 Impact Factor
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    ABSTRACT: A handwritten Chinese character recognition method based on primitive and compound fuzzy features using the SEART neural network model is proposed. The primitive features are extracted in local and global view. Since handwritten Chinese characters vary a great deal, the fuzzy concept is used to extract the compound features in structural view. We combine the two categories of features and use a fast classifier, called the Supervised Extended ART (SEART) neural network model, to recognize handwritten Chinese characters. The SEART classifier has excellent performance, is fast, and has good generalization and exception handling abilities in complex problems. Using the fuzzy set theory in feature extraction and the neural network model as a classifier is helpful for reducing distortions, noise and variations. In spite of the poor thinning, a 90.24% recognition rate on average for the 605 test character categories was obtained. The database used is CCL/HCCR3 (provided by CCL, ITRI, Taiwan). The experiment not only confirms the feasibility of the proposed system, but also suggests that applying the fuzzy set theory and neural networks to recognition of handwritten Chinese characters is an efficient and promising approach.
    Applied Intelligence 01/1998; 8:269-285. · 1.85 Impact Factor
  • Jyh-Ming Chen, Shuan-Hao Wu, Hahn-Ming Lee
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    ABSTRACT: This research is based on a fuzzy neural network, named knowledge-based neural network with trapezoid fuzzy set inputs (KBNN/TFS). We use this network model to refine fuzzy rules with a training database. We propose an interactive consistency checking engine with automatic rule insertion and deletion (ICE/RID) to perform fuzzy rule verification. This process is used to verify the initial rule base and the rules refined by KBNN/TFS. With the interactive interface of ICE, we can detect redundant rules, subsumed rules, and conflict rules. Besides, we can also use RID to insert and delete fuzzy rules automatically if necessary. The proposed model is tested with an inverted pendulum system (IPS). In these experiments, we demonstrate the ability of ICE/RID to remove inconsistencies and insert rules in KBNN/TFS. With the combination of ICE/RID and KBNN/TFS, a valid and consistent rule base can be obtained
    Fuzzy Systems Symposium, 1996. 'Soft Computing in Intelligent Systems and Information Processing'., Proceedings of the 1996 Asian; 01/1997
  • Hahn-Ming Lee, Fu-Tyan Lin, Jyh-Ming Chen
    J. Inf. Sci. Eng. 01/1997; 13:311-333.
  • Source
    Tahn-Ming Lee, Jyh-Ming Chen, En-Chieh Chang
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    ABSTRACT: In this paper, a fuzzy neural network model, named knowledge-based neural network with trapezoid fuzzy set (KBNN/TFS), that processes trapezoid fuzzy inputs is proposed. In addition to fuzzy rule revision, the model is capable of fuzzy rule verification and generation. To facilitate the processing of fuzzy information, LR-fuzzy interval is employed. Imperfect domain theories can be directly translated into KBNN/TFS structure and then revised by neural learning. A consistency checking algorithm is proposed for verifying the initial knowledge and the revised fuzzy rules. The algorithm is aimed at finding the redundant rules, conflicting rules and subsumed rules in fuzzy rule base. We show the workings of the proposed model on a knowledge base evaluator. The result show that the proposed algorithm can detect the inconsistencies in KBNN/TFS. By removing the inconsistencies and applying a rule insertion mechanism, the results are greatly improved. Besides, a consistent fuzzy rule base is obtained
    Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on; 10/1996

Publication Stats

161 Citations
6.19 Total Impact Points


  • 1998
    • National Taiwan University of Science and Technology
      • Department of Electronic and Computer Engineering
      T’ai-pei, Taipei, Taiwan