• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.401-408
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• 1997

Human gets almist all of his knoweledge from the recognition and the accumulation of input patterns,image or sound,the he gets theough his eyes and through his ears.Among these means,his chracter recognition,an ability that allows him to recognize characters and understand their meanings through visual information, is now applied to a pattern recognition system using neural network in computer. Recurrent neural network is one of those models that reuse the output value in neural network learning.Recently many studies try to apply this recurrent neural network to the classification of static patterns like off-line handwritten characters. But most of their efforts are not so drrdtive until now.This stusy suggests a new type of recurrent neural network for an deedctive classification of the static patterns such as off-line handwritten chracters.Using the new J-E(Jordan-Elman)neural network model that enlarges and combines Jordan Model and Elman Model,this new type is better than those of before in recobnizing the static patterms such as figures and handwritten-characters.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.113-123
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• 2003

Many researches on the application of neural networks for making financial index prediction have proven their advantages over statistical and other methods. In this paper, a neural network model is proposed for the Buying, Holding or Selling timing prediction in stocks by the price index of stocks by inputting the closing price and volume of dealing in stocks and the technical indexes(MACD, Psychological Line). This model has an equalized multi-layer arithmetic function as well as the time series prediction function of backpropagation neural network algorithm. In the case that the numbers of learning data are unbalanced among the three categories (Buying, Holding or Selling), the neural network with conventional method has the problem that it tries to improve only the prediction accuracy of the most dominant category. Therefore, this paper, after describing the structure, working and learning algorithm of the neural network, shows the equalized multi-layer arithmetic method controlling the numbers of learning data by using information about the importance of each category for improving prediction accuracy of other category. Experimental results show that the financial index prediction using the equalized multi-layer arithmetic neural network has much higher correctness rate than the other conventional models.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.1
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• pp.14-24
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• 2002

In this paper, a design of the pulsewidth-modulation(PWM) neural network with both retrieving and learning function is proposed. In the designed PWM neural system, the input and output signals of the neural network are represented by PWM signals. In neural network, the multiplication is one of the most commonly used operations. The multiplication and summation functions are realized by using the PWM technique and simple mixed-mode circuits. Thus, the designed neural network only occupies the small chip area. By applying some circuit design techniques to reduce the nonideal effects, the designed circuits have good linearity and large dynamic range. Moreover, the delta learning rule can easily be realized. To demonstrate the learning capability of the realized PWM neural network, the delta learning nile is realized. The circuit with one neuron, three synapses, and the associated learning circuits has been designed. The HSPICE simulation results on the two learning examples on AND function and OR function have successfully verified the function correctness and performance of the designed neural network.

• Journal of Digital Convergence
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• v.17 no.7
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• pp.285-292
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• 2019

With the popularization of PC, SNS and IoT, a lot of data is generated and the amount is increasing exponentially. Artificial neural network learning is a topic that attracts attention in many fields in recent years by using huge amounts of data. Artificial neural network learning has shown tremendous potential in speech recognition and image recognition, and is widely applied to a variety of complex areas such as medical diagnosis, artificial intelligence games, and face recognition. The results of artificial neural networks are accurate enough to surpass real human beings. Despite these many advantages, privacy problems still exist in artificial neural network learning. Learning data for artificial neural network learning includes various information including personal sensitive information, so that privacy can be exposed due to malicious attackers. There is a privacy risk that occurs when an attacker interferes with learning and degrades learning or attacks a model that has completed learning. In this paper, we analyze the attack method of the recently proposed neural network model and its privacy protection method.

• Journal of Advanced Navigation Technology
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• v.25 no.5
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• pp.409-414
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• 2021

The growing concerns on the emission of particulate matter has prompted a demand for highly reliable particulate matter forecasting. Currently, several studies on particulate matter prediction use various deep learning algorithms. In this study, we compared the predictive performances of typical neural networks used for particulate matter prediction. We used deep neural network(DNN), recurrent neural network, and long short-term memory algorithms to design an optimal predictive model on the basis of a hyperparameter search. The results of a comparative analysis of the predictive performances of the models indicate that the variation trend of the actual and predicted values generally showed a good performance. In the analysis based on the root mean square error and accuracy, the DNN-based prediction model showed a higher reliability for prediction errors compared with the other prediction models.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.13-21
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• 2002

Fuzzy logic ignores some information in the reasoning process. Neural network is powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule-inference network. And the traditional propagation rule is modified. Experiments are performed to compare search costs by sequential searching and searching by priority. The experimental results show that the searching by priority is more efficient than the sequential searching as the size of the fuzzy inference network becomes larder and an the number of searching increases.

• The Journal of Information Technology
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• v.8 no.1
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• pp.51-56
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• 2005

The Back propagation algorithm is a very effective supervised training method for multi-layer feed forward neural networks. This paper studies the decision boundary formation based on the Back propagation algorithm. The discriminating powers of several neural network topology are also investigated against five manually created data sets. It is found that neural networks with multiple hidden layer perform better than single hidden layer.

• Speech Sciences
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• v.11 no.1
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• pp.21-29
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• 2004

In this paper we examined the effect of normalization on discriminating the pathological voice into normal and abnormal classes using artificial neural network. Average values per each parameter were used to normalize each set of parameter values. Artificial neural networks were used as classifiers. And the effect of normalization was evaluated by comparing the discrimination results between original and normalized parameter sets.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1282-1291
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• 1993

The color image of the rose was segmented by the cluster analysis on the color space into the characteristic sub-regions, the degree of bloom of the flower was represented numerically base on the segmented image and judged by the artificial neural network system whose input variable were the characteristic regions. Judgement by neural system were compared with that of the farmers and it was found that degree of agreement were fairly good.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.131-138
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• 1998

We derive a back-propagation learning algorithm of fuzzy neural networks using fuzzy operations, which preserves the shapes of fuzzy numbers, in order to utilize fuzzy if-then rules as well as numerical data in the learning of neural networks for classification problems and for fuzzy control problems. By introducing the shape preseving fuzzy operation into a neural network, the proposed network simplifies fuzzy arithmetic operations of fuzzy numbers with exact result in learning the network. And we illustrate our approach by computer simulations on numerical examples.