• 한국지능시스템학회논문지
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• 제17권7호
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• pp.970-976
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• 2007

본 연구에서는 퍼지 추론 메커니즘에 기반 한 다항식 네트워크 패턴 분류기(Polynomial Network Pattern Classifier; PNC)를 설계하고 Particle Swarm Optimization 알고리즘을 이용하여 PNC 파라미터, 즉, 학습률, 모멘텀 계수, FCM 클러스터링의 퍼지화 계수(fuzzification Coefficient)를 최적화한다. 제안된 PNC 구조는 FCM 클러스터링에 기반한 분할 함수를 활성 함수로 사용하며, 다항식 함수로 구성된 연결가중치를 사용함으로서 기존 신경회로망 분류기의 선형적인 특성을 개선한다. PNC 구조는 언어적 해석관점에서 "If-then"의 퍼지 규칙으로 표현되며 퍼지 추론 메커니즘에 의해 구동된다. 즉 조건부, 결론부, 추론부 세 가지의 기능적 모듈로 나뉘어 네트워크 구조가 형성된다. 조건부는 FCM 클러스터링을 사용하여 입력 공간을 분할하고, 결론부는 분할된 로컬 영역을 다항식 함수로 표현한다. 마지막으로, 네트워크의 최종출력은 추론부의 퍼지추론에 의한다. 제안된 PNC는 다항식 기반 구조의 퍼지 추론 특성으로 인해 출력 공간상에 비선형 판별 함수(nonlinear discernment function)가 생성되어 분류기로서의 성능을 높인다.

• 대한전자공학회논문지SD
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• 제45권1호
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• pp.20-27
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• 2008

본 논문에서는 비선형 패턴 분류를 위해 FPGA 칩에 신경회로망을 구현하였다. 병렬처리 연산을 위해 순방향 신경회로망이 구현 되었다. 신경망의 학습을 off-line으로 한 다음에 가중치 값들을 저장하여 사용한다. 예로서, AND와 XOR 논리의 패턴 구분이 수행된다. 실험결과를 통해 FPGA에 구현된 신경회로망이 잘 작동하는 것을 검증하였다.

• 한국지능시스템학회논문지
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• 제10권2호
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• pp.75-82
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• 2000

이논문에서는 영상처리 기법과 신경회로망 분류기를 이용하여 냉연강판의 표면에 나타나는 결함을 검출하는 기법을 제안한다. 냉연강판 표면의 입력영상을 웨이블렛 변환하여 영상신호 데이터량을 감소시키고 영상신호의 저주파수 영역에 해당하는 부분영상으로부터 구한 co-occurrence 행렬을 이용하여 주된 특징들을 추출한 후 신경회로망 분류기를 이용하여 표면결함을 분류하는 과정을 밟는다 현장에서 직접입수한 실제 냉연강판 표면결함 영상에 대하여 결함의 검출 및 분류기법을 제시하고 실험을 통해 기존의 백터양자화 기법과 비교하여 우수한 성능을 보임을 입증하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.883-884
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• 2008

This paper presents a method for classification of underwater transient signals using, which employs a binary image pattern of the mel-frequency cepstral coefficients(MFCC) as a feature vector and a neural network as a classifier. A feature vector is obtained by taking DCT and 1-bit quantization for the square matrix of the MFCC sequences. The classifier is a feed-forward neural network having one hidden layer and one output layer, and a back propagation algorithm is used to update the weighting vector of each layer. Experimental results with some underwater transient signals demonstrate that the proposed method is very promising for classification of underwater transient signals.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권7호
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• pp.525-530
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• 2004

A classifier was constructed by using a generalized regression neural network (GRU) and random generator (RG), which was applied to classify DNA sequences. Three data sets evaluated are eukaryotic and prokaryotic sequences (Data-I), eukaryotic sequences (Data-II), and prokaryotic sequences (Data-III). For each data set, the classifier performance was examined in terms of the total classification sensitivity (TCS), individual classification sensitivity (ICS), total prediction accuracy (TPA), and individual prediction accuracy (IPA). For a given spread, the RG played a role of generating a number of sets of spreads for gaussian functions in the pattern layer Compared to the GRNN, the RG-GRNN significantly improved the TCS by more than 50%, 60%, and 40% for Data-I, Data-II, and Data-III, respectively. The RG-GRNN also demonstrated improved TPA for all data types. In conclusion, the proposed RG-GRNN can effectively be used to classify a large, multivariable promoter sequences.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.555-559
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• 1998

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis have been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, We proposed an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted four morphological features parameters such as centromeric index (C.I.), relative length ratio(R.L.), relative area ratio(R.A.) and chromosome length(C.L.). These Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results shown that the chromosome classification error was reduced much more than that of the other classification methods.

• 융합신호처리학회논문지
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• 제16권4호
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• pp.134-138
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• 2015

본 논문은 심장질환을 비침습적 방법으로 빠르고 쉽게 진단할 수 있도록 심음을 이용하는 방법에 대한 가능성을 찾는 것이다. 일반적으로 심음의 분류를 위하여 심음을 분리한 후에 특징파라미터를 추출하는 과정을 거치지 않고, 심음 분리에 사용되는 Shannon 엔트로피로 정규화하여 신경회로망의 입력으로 사용하였다. 심장질환에 따른 심잡음 분류를 위하여 Scaled conjugate gradient 역전파 알고리즘을 이용하여 신경회로망 분류기를 구현하였다. 정상 심음과 심장 질환의 경우 5가지를 포함하여 6종류의 심잡음에 대하여 분류가 가능함을 확인하였다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1999년도 가을 학술발표논문집 Vol.26 No.2 (2)
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• pp.551-553
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• 1999

This paper explores the utility of a new classifier fusion approach to discrimination. Multiple classifier fusion, a popular approach in the field of pattern recognition, uses estimates of each individual classifier's local accuracy on training data sets. In this paper we investigate the effectiveness of fusion methods compared to individual algorithms, including the artificial neural network and k-nearest neighbor techniques. Moreover, we propose an efficient meta-classifier architecture based on an approximation of the posterior Bayes probabilities for learning the oracle.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2226-2229
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• 2003

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. In GA optimization, neuron spreads were represented in a chromosome. The proposed optimization method was applied to a data set, consisted of 4 different promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The range of neuron spreads was experimentally varied from 0.4 to 1.4 with an increment of 0.1. The GA-GRNN was compared to a conventional GRNN. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. The GA-GRNN significantly improved the total classification sensitivity compared to the conventional GRNN. Also, the GA-GRNN demonstrated an improvement of about 10.1% in the total prediction accuracy. As a result, the proposed GA-GRNN illustrated improved classification sensitivity and prediction accuracy over the conventional GRNN.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.133-136
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• 1997

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room or improving the accuracy of chromosome classification. In this paper, We propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of multi-step multi-layer neural network(MMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted three morphological features parameters such as centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.). This Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results show that the chromosome classification error is reduced much more than that of the other classification methods.