• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.120-123
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• 1997

In this paper, we propose the Fuzzy Inference-based Reinforcement Learning Algorithm. We offer more similar learning scheme to the psychological learning of the higher animal's including human, by using Fuzzy Inference in Reinforcement Learning. The proposed method follows the way linguistic and conceptional expression have an effect on human's behavior by reasoning reinforcement based on fuzzy rule. The intervals of fuzzy membership functions are found optimally by genetic algorithms. And using Recurrent state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying to the inverted pendulum control problem.

• 한국기계연구소 소보
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• s.19
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• pp.93-115
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• 1989

Cerebellar Model Arithmetic Controller(CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d. o. f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process; A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.191-196
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• 1997

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-Means(FCM) algorithm is most frequently used for fuzzy clustering. But some fixed point of FCM algorithm, know as Tucker's counter example, is not a reasonable solution. Moreover, FCM algorithm is impossible to perform the on-line learning since it is basically a batch learning scheme. This paper presents unsupervised learning networks as an attempt to improve shortcomings of the conventional clustering algorithm. This model integrates optimization function of FCM algorithm into unsupervised learning networks. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of a fuzzy objective function. Using the result of formal derivation, two algorithms of fuzzy cluster analysis, the batch learning version and on-line learning version, are devised. They are tested on several data sets and compared with FCM. The experimental results show that the proposed algorithms find out the reasonable solution on Tucker's counter example.

• The Journal of Korean Association of Computer Education
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• v.11 no.2
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• pp.107-117
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• 2008

The agent system that can adjust the workload dynamically through thc periodical monitoring of the VoD system comprises the agency part interfacing the VoD system and the intelligence part reasoning or learning the facts required for the adjustment of workload. This paper proposes a learning method that can apply to the intelligence part of the agent system. The proposed method can adjust the workload more efficiently by the rule's reasoning process and case-based learning process. An experiment of implementing a simulator was conducted to see whether or not application of the proposed method to VoD systems is efficient. As a result of the experiment, it was found that the throughput and the average waiting time of the VoD server were relatively improved when the proposed method was applied compared to existing means.

• Journal of The Korean Association For Science Education
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• v.17 no.4
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• pp.501-510
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• 1997

Science teachers' beliefs about science and school science and their perceptions of the science laboratory learning environment were investigated with an assumption that science laboratory teaching would be affected by science teachers' beliefs. Likert-scale questionnaires of BASSSQ and SLEI were used in this study. The major findings were as follows: 1. Science teachers showed inconsistent beliefs about science and school science. Their responses reflected a patch-like view of postmodern epistemology and objectivism They also showed somewhat different views about science and school science. It was found that science teachers had strong objectivist views about science in some parts. but they had moderate constructivist views about school science in other parts; 2. The mean scores of student cohesiveness, integration. and rule clarity on the actual version in SLEl were relatively high, but those of open-endedness and physical environment were very low; 3. There was no association between teachers' beliefs about science and their perceptions of the science laboratory learning environment. But some associations were found between teachers' beliefs about school science and their perception on student cohesiveness, integration, and rule clarity of the actual science laboratory learning environment. Teachers' beliefs about school science had some statistically significant correlations with their perceptions on all scales of the preferred version of SLEI. We could not show a causal relationship between teachers' beliefs and their science laboratory learning environment through these results. But it can be suggested that teachers' beliefs about school science do have a role in constructing a desirable science laboratory learning environment, as we found that there were statistically significant correlations between them.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.173-183
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• 1998

In this paper, an auto-tuning method for fuzzy controller's membership functions based on the neural network is presented. The neural network emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and the reformed fuzzy controller uses for speed control of induction motor. Thus, in the case of motor parameter variation, the proposed method is superior to a conventional method in the respect of operation time and system performance. 32bit micro-processor DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzzy control algorithm. Through computer simulation and experimental results, it is confirmed that the proposed method can provide more improved control performance than that PI controller and conventional fuzzy controller.

• Journal of The Korean Association For Science Education
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• v.16 no.3
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• pp.295-302
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• 1996

The purpose of this study was to investigate the effects of training and use of worked-example on chemical problem-solving learning. Schema acquisition and rule automation are the basic components of skilled problem-solving, which are dependent on appropriately focused attention and sufficient cognitive resources. Training and use of worked-example facilitate schema acquisition and rule automation, so improve problem-solving learning. The subjects of this study were 60 high school students. The average age was 17 years old. Then, they were randomly assigned to each groups and the chemical reaction problems used as experimental materials. The independent variables of this study were training and use of worked-examples and dependent variables were time for solution and the number of error. The results of this study were as follows; 1. The worked-example groups spent significantly less time on solution for acquisition problems than the conventional problem groups. 2. The long-acquisition groups spent significantly less time on solution for acquisition problems than the short-acquisition groups. 3. The modified worked-example groups did not spend significantly less time on solution for acquisition problems than the worked-example groups.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.138-145
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• 2003

Performance assessment was introduced to improvement of self-directed learning and method of assessment for differenced learning as the seventh educational curriculum is enforced. Performance assessment is overcoming limitation about problem solving ability and higher thinking abilities assessment that is problem of a written examination and get into the spotlight by way for quality of class and school normalization. But, performance assessment has problems about possibilities of assessment fault by appraisal, fairness, reliability, and validity of grading, ambiguity of grading standard, difficulty about objectivity security etc. This study proposes fuzzy performance assessment system to solve problem of the conventional performance assessment. This paper presented an objective and reliable performance assessment method through fuzzy reasoning, design fuzzy membership function and define fuzzy rule analyzing factor that influence in each sacred ground of performance assessment to account principle subject. Also, performance assessment item divides by formation estimation and subject estimation and designed membership function in proposed performance assessment method. Performance assessment result that is worked through fuzzy performance assessment system can pare down burden about appraisal's fault and provide fair and reliable assessment result through grading that have correct standard and consistency to students.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.52-57
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• 2003

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection. The classifier system of Holland can evaluate the usefulness of rules represented by classifiers with repeated learning. FCS makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies the method of machine learning to the concept of fuzzy logic. It is that the antecedent and consequent of classifier is same as a fuzzy rule. In this paper, the FCS is the Michigan style. A single fuzzy if-then rule is coded as an individual. The average gray levels which each group of neighbor pixels has are represented into fuzzy set. Then a pixel is decided whether it is edge pixel or not using fuzzy if-then rules. Depending on the average of gray levels, a number of fuzzy rules can be activated, and each rules makes the output. These outputs are aggregated and defuzzified to take new gray value of the pixel. To evaluate this edge detection, we will compare the new gray level of a pixel with gray level obtained by the other edge detection method such as Sobel edge detection. This comparison provides a reinforcement signal for FCS which is reinforcement learning. Also the FCS employs the Genetic Algorithms to make new rules and modify rules when performance of the system needs to be improved.