• 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 IEEK Conference
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• 2000.07b
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• pp.1029-1032
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• 2000

A neuro-fuzzy based circuit to recognize circuit pat-terns is proposed in this paper. The simple algorithm and exemption from the use of template patterns as well as multipliers enable the proposed circuit to implement on the hardware of an economical scale. Furthermore, thanks to the circuit design by using current-mode techniques, the proposed circuit call achieve easy extendability of tile circuit and efficient pattern recognition with high-speed. The validity of the proposed algorithm and tile circuit design is confirmed by computer simulations. The proposed pattern recognition circuit is integrable by a standard CMOS technology.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.5 no.1
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• pp.49-53
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• 1980

Since Zadeh had published the fuzzy set theory at 1965, it has been applied to many fields such as realizability of communication nets, automatic control, learning systems, switching circuits. In this paper, the method of applying a fuzzy logic to a pattern classification is studied and the difference of fuzzy logic from Boolean algebra is discussed. Classfication experiment is carried out 16 persons' photos of three families by fourty male and female observers and recognition rate 94% is obtained.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.587-599
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• 1995

This paper proposes a method for pattern recognition using spectral analysis and a hybrid genetic-neural-fuzzy networks. The feature vectors using spectral analysis on contour sequences of 2-D images are extracted, and the vectors are not effected by translation, rotation and scale variance. A combined model using the advantages of conventional method is proposed, those are supervised learning BP, global searching genetic algorithm, and unsupervised learning fuzzy c-method. The proposed method is applied to 10 aircraft recognition to confirm the performance of the method. The experimental results show that the proposed method is better accuracy than conventional method using BP or fuzzy c-method, and learning speed is enhanced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.721-724
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• 2014

Ultrasonic sensor is used widely for many applications because low cost, simple structure, and low restriction. There are many difficulties to recognize an object by use an ultrasonic sensor, because of low resolution, poor direction, and measurement error. To improve the these problem, we use the various kinds of sensor arrangement methods, large amount of sensor, and change the arrangement pattern of sensor. In this paper, to obtain the most basic parameters for pattern recognition such as distance, dimension of the object, an angle of the object, we get the improved results by use the intelligent calculation algorithm based on Neuro-Fuzzy. This method use the multifarious output voltage of ultrasonic sensor by simple electronic circuit.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.213-219
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• 2004

This paper proposes speaker recognition algorithm which includes the pitch parameter for the peak and valley. The time-frequency hybrid method for pitch extraction is valuable in that it can improve resolution in the time domain and accuracy in the frequency domain at the same time. It makes reference pattern using membership function and performs vocal track recognition of common character using fuzzy pattern matching in order to include time variation width for non-linear utterance for proposed method, speaker recognition experiments are carried out using vowels and number sounds.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.507-513
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• 2004

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.439-441
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• 1993

A tire tread pattern recognition scheme of which the pattern recognition algorithm is designed based on the fuzzy hierarchical clustering method is proposed and compared with the scheme based on the conventional FCM. The features are extracted from the binary images of the tire tread patterns. In the proposed scheme, the protoypes are obtained more easily and schematically than obtained prototypes using FCM. The experimental results of classification for the practical situations are given and shows the usefulness of the proposed scheme.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.11
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• pp.2711-2722
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• 1993

Most of the current 2-D object recognition systems are model-based. In such systems, the representation of each of a known set of objects are precompiled and stored in a database of models. Later, they are used to recognize the image of an object in each instance. In this thesis, the approach method for the 2-D object recognition is treating an object boundary as a string of structral units and utilizing string matching to analyze the scenes. To reduce string matching time, models are rebuilt by means of fuzzy c-means clustering algorithm. In this experiments, the image of objects were taken at initial position of a robot from the CCD camera, and the models are consturcted by the proposed algorithm. After that the image of an unknown object is taken by the camera at a random position, and then the unknown object is identified by a comparison between the unknown object and models. Finally, the amount of translation and rotation of object from the initial position is computed.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.14-18
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• 2002

The perceptron algorithm, which is one of a class of gradient descent techniques, has been widely used in pattern recognition to determine linear decision boundaries. However, it may not give desirable results when pattern sets are nonlinerly separable. A fuzzy version was developed to male up for the weaknesses in the crisp perceptron algorithm. This was achieved by assigning memberships to the pattern sets. However, still another drawback exists in that the pattern memberships do not consider class typicality of the patterns. Therefore, we propose a possibilistic approach to the crisp perceptron algorithm. This algorithm combines the linearly separable property of the crisp version and the convergence property of the fuzzy version. Several examples are given to show the validity of the method.