• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.69-76
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• 2008

This paper concerns Fuzzy Radial Basis Function Neural Network (FRBFNN) and automatic rule generation of extraction of the FRBFNN by means of mountain clustering. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values (degree of membership) directly rely on the computation of the relevant distance between data points. Also, we consider high-order polynomial as the consequent part of fuzzy rules which represent input-output characteristic of sup-space. The number of clusters and the centers of clusters are automatically generated by using mountain clustering method based on the density of data. The centers of cluster which are obtained by using mountain clustering are used to determine a degree of membership and weighted least square estimator (WLSE) is adopted to estimate the coefficients of the consequent polynomial of fuzzy rules. The effectiveness of the proposed model have been investigated and analyzed in detail for the representative nonlinear function.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.3
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• pp.4-19
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• 1991

In this paper, we propose a fuzzy neural network pattern classifier utilizing fuzzy information. This system works without any a priori information about the number of clusters or cluster centers. It classifies each input according to the distance between the weights and the normalized input using Bezdek's [1] fuzzy membership value equation. This model returns the correct membership value for each input vector and find several cluster centers. Some experimental studies of comparison with other algorithms will be presented for sample data sets.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.77-85
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• 1993

In this paper, we present a method to solve a convexly combined fuzzy relational equation with generalized connectives. For this, we propose a neural network whose structure represents the fuzzy relational equation. Then we derive a learning algorithm by using the concept of back-propagation learning. Since the proposed method can be used for a general form of fuzzy relational equations, such fuzzy max-min or min-max relational equations can be treated as its special cases. Moreover, the relational structure adopted in the proposed neurocomputational approach can work in a highly parallel manner so that real-time applications of fuzzy sets are possibles as in fuzzy logic controllers, knowledge-based systems, and pattern recognition systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.399-403
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• 1998

In this paper, we proposed a new fuzzy supervised learning algorithm. We construct, and train, a new type fuzzy neural net to model the linear activation function. Properties of our fuzzy neural net include : (1) a proposed linear activation function ; and (2) a modified delta rule for learning algorithm. We applied this proposed learning algorithm to exclusive OR,3 bit parity using benchmark in neural network and pattern recognition problems, a kind of image recognition.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.114-117
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• 2000

In this paper, Fuzzy Self-organizing Neural Networks(FSONN) based on Fuzzy Neural Networks(FNN) is proposed to overcome some problems, such as the conflict between ovefitting and good generation, and low reliability. The proposed FSONN consists of FNN and SONN. Here, FNN is used as the premise part of FSONN and SONN is the consequnt part of FSONN. The FUN plays the preceding role of FSONN. For the fuzzy reasoning and learning method in FNN, Simplified fuzzy reasoning and backpropagation learning rule are utilized. The number of layers and the number of nodes in each layers of SONN that is based on the GMDH method are not predetermined, unlike in the case of the popular multi layer perceptron structure and can be generated. Also the partial descriptions of nodes can use various forms such as linear, modified quadratic, cubic, high-order polynomial and so on. In this paper, the optimal design procedure of the proposed FSONN is shown in each step and performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.215-221
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• 2003

Generally, we want to be searched the newest as well as some appropriate personalized information from the internet resources. However, it is a complex and repeated procedure to search some appropriate information. Moreover, because the user's interests are changed as time goes, the real time modeling of a user's interests should be necessary. In this paper, I propose PREA system that can search and filter documents that users are interested from the World Wide Web. And then it constructs the user's interest model by a modified spreading neural network. Based on this network, PREA can easily produce some queries to search web documents, and it ranks them. The conventional spreading neural network does not have a visualization function, so that the users could not know how to be configured his or her interest model by the network. To solve this problem, PREA gives a visualization function being shown how to be made his interest user model to many users.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.355-364
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• 1994

The most important requirement for the controller of a prosthetic arm is that it has a high fidelity discriminator where the motion control may be performed open loop using EMG signals as a control source. Therefore, it is very effective method to reduce the influence of misclassification of classifier for the total system performance. This paper presents the new function discrimination method which combines MLP classifier and frizzy filter by stages for the requirement. The major advantage of MLP is a consistent learning capability for the easy adaptation to environments. The fuzzy filter uses all informations of MLP outputs and prior EMG activity informations which increase as the experience increases. That property is superior to one which uses maximum output of MLP in view of information amounts and quality. Simulation result shows that proposed method is superior to the probabilistic model, MLP model and the combined model of both in the respect of discrimination quaity.

• Journal of Digital Contents Society
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• v.2 no.1
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• pp.51-61
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• 2001

The FLC(Fuzzy Logic Controller) is stronger to the disturbance and has the excellent characteristic to the overshoot of the initialized value than the classical controller, and also can carry out the proper control being out of all relation to the mathematical model and parameter value of the system. But it has the restriction which can't adopt the environment changes of the control system because of generating the fuzzy control rule through an expert's experience and the fixed value of the once determined control rule, and also can't converge correctly to the desired value because of haying the minute error of the controller output value. Now there are many suggested methods to eliminate the minute error, we also suggest the GA-FNNIC(Genetic Algorithm Fuzzy Neural Network Intelligence Controller) combined FLC with NN(Neural Network) and GA(Genetic Algorithm). In this paper, we compare the suggested GA-FNNIC with FLC and analyze the output characteristics, convergence speed, overshoot and rising time. Finally we show that the GA-FNNIC converge correctly to the desirable value without any error.