• Title/Summary/Keyword: 신경회로망

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Automatic Recognition of Pitch Accent Using Distributed Time-Delay Recursive Neural Network (분산 시간지연 회귀신경망을 이용한 피치 악센트 자동 인식)

  • Kim Sung-Suk
    • The Journal of the Acoustical Society of Korea
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    • v.25 no.6
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    • pp.277-281
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    • 2006
  • This paper presents a method for the automatic recognition of pitch accents over syllables. The method that we propose is based on the time-delay recursive neural network (TDRNN). which is a neural network classifier with two different representation of dynamic context: the delayed input nodes allow the representation of an explicit trajectory F0(t) along time. while the recursive nodes provide long-term context information that reflects the characteristics of pitch accentuation in spoken English. We apply the TDRNN to pitch accent recognition in two forms: in the normal TDRNN. all of the prosodic features (pitch. energy, duration) are used as an entire set in a single TDRNN. while in the distributed TDRNN. the network consists of several TDRNNs each taking a single prosodic feature as the input. The final output of the distributed TDRNN is weighted sum of the output of individual TDRNN. We used the Boston Radio News Corpus (BRNC) for the experiments on the speaker-independent pitch accent recognition. π 1e experimental results show that the distributed TDRNN exhibits an average recognition accuracy of 83.64% over both pitch events and non-events.

A Control Method using the modified Elman Neural Network (변형된 Elman 신경회로망을 이용한 제어방식)

  • 최우승;김주동
    • Journal of the Korea Society of Computer and Information
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    • v.4 no.3
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    • pp.67-72
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    • 1999
  • The neural network is a static network that consists of a number of layer: input layer, output layer and one or more hidden layer connected in a feed forward way. The popularity of neural network appear to be its ability of learning and approximation capability. The Elman Neural Network proposed the J. Elman. is a type of recurrent network. Is has the feedback links from hidden layer to context layer. So Elman Neural Network is the better performance than the neural network. In this paper. we propose the Modified Elman Neural Network. The structure of a MENN is based on the basic ENN. The recurrency of the network is due to the feedback links from the output layer and the hidden layer to the context layer. In order to certify the usefulness or the proposed method. the MENN apply to the multi target system. Simulation shows that the proposed MENN method is better performance than the multi layer neural network and ENN.

Optimum Design of Neural Networks for Flight Control System (신경회로망 구조 최적화를 통한 비행제어시스템 설계)

  • Choe,Gyu-Ho;Choe,Dong-Uk;Kim,Yu-Dan
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.31 no.7
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    • pp.75-84
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    • 2003
  • To reduce the effects of the uncertainties due to the modeling error and aerodynamic coefficients, a nonlinear adaptive control system based on neural networks is proposed . Neural networks parameters are adjusted by using an adaptive law. The sliding mode control scheme is used to compensate for the effect of the approximation error of neural networks. Control parameters and neural networks structures are optimized to obtain better performance by using the genetic algorithm. By introducing the concept of multi-groups of populations, the genetic algorithm is modified so that individuals and groups can be simultaneously evolved . To verify the performance of the pro posed algorithm, the optimized neural networks control system is applied to an aircraft longitudinal dynamics.

Fuzzy Neural Network Model Using Asymmetric Fuzzy Learning Rates (비대칭 퍼지 학습률을 이용한 퍼지 신경회로망 모델)

  • Kim Yong-Soo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.7
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    • pp.800-804
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    • 2005
  • This paper presents a fuzzy learning rule which is the fuzzified version of LVQ(Learning Vector Quantization). This fuzzy learning rule 3 uses fuzzy learning rates. instead of the traditional learning rates. LVQ uses the same learning rate regardless of correctness of classification. But, the new fuzzy learning rule uses the different learning rates depending on whether classification is correct or not. The new fuzzy learning rule is integrated into the improved IAFC(Integrated Adaptive Fuzzy Clustering) neural network. The improved IAFC neural network is both stable and plastic. The iris data set is used to compare the performance of the supervised IAFC neural network 3 with the performance of backprogation neural network. The results show that the supervised IAFC neural network 3 is better than backpropagation neural network.

Daily Stock Price Forecasting Using Deep Neural Network Model (심층 신경회로망 모델을 이용한 일별 주가 예측)

  • Hwang, Heesoo
    • Journal of the Korea Convergence Society
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    • v.9 no.6
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    • pp.39-44
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    • 2018
  • The application of deep neural networks to finance has received a great deal of attention from researchers because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from large sets of data, which is required to describe nonlinear input-output relations of financial time series. The paper presents a new deep neural network model where single layered autoencoder and 4 layered neural network are serially coupled for stock price forecasting. The autoencoder extracts deep features, which are fed into multi-layer neural networks to predict the next day's stock closing prices. The proposed deep neural network is progressively learned layer by layer ahead of the final learning of the total network. The proposed model to predict daily close prices of KOrea composite Stock Price Index (KOSPI) is built, and its performance is demonstrated.

Human Activity Recognition using Multi-temporal Neural Networks (다중 시구간 신경회로망을 이용한 인간 행동 인식)

  • Lee, Hyun-Jin
    • Journal of Digital Contents Society
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    • v.18 no.3
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    • pp.559-565
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    • 2017
  • A lot of studies have been conducted to recognize the motion state or behavior of the user using the acceleration sensor built in the smartphone. In this paper, we applied the neural networks to the 3-axis acceleration information of smartphone to study human behavior. There are performance issues in applying time series data to neural networks. We proposed a multi-temporal neural networks which have trained three neural networks with different time windows for feature extraction and uses the output of these neural networks as input to the new neural network. The proposed method showed better performance than other methods like SVM, AdaBoot and IBk classifier for real acceleration data.

Adaptive Nonlinear Control of Helicopter Using Neural Networks (신경회로망을 이용한 헬리콥터 적응 비선형 제어)

  • Park, Bum-Jin;Hong, Chang-Ho;Suk, Jin-Young
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.32 no.4
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    • pp.24-33
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    • 2004
  • In this paper, the helicopter flight control system using online adaptive neural networks which have the universal function approximation property is considered. It is not compensation for modeling errors but approximation two functions required for feedback linearization control action from input/output of the system. To guarantee the tracking performance and the stability of the closed loop system replaced two nonlinear functions by two neural networks, weight update laws are provided by Lyapunov function and the simulation results in low speed flight mode verified the performance of the control system with the neural networks.

Performance Comparison of Various Features for Off-line Handwritten Numerals Recognition and Suggestion for Improving Recognition rate for Using Majority Voting (오프라인 필기체 숫자인식을 위한 특징 비교 및 다수결 투표를 사용한 성능향상 방안)

  • 권영일;하진영
    • Proceedings of the Korean Information Science Society Conference
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    • 2003.10b
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    • pp.595-597
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    • 2003
  • 오프라인 필기체 숫자 인식에서 다양한 변형을 잘 흡수 할 수 있는 효율적인 특징을 찾는 것은 중요한 일이며, 본 논문에서는 이를 위해 다양한 단일특징들을 구현 하였으며, 단일 특징만으로는 만족 할 만한 성능을 기대하기 어렵기 때문에 다양한 단일 특징을 복합특징으로 구성하였다. 또한 오프라인 필기체 숫자인식에서 좋은 성능을 발휘하는 것으로 알려진 신경회로망으로 학습을 하였으며, 인식의 성능을 개선시키기 위해 효과적인 특징을 조합하여 하나의 단일 신경회로망들을 구성하고 그것을 다시 복합신경회로망으로 구성하여 성능을 실험 함으로서 성능의 향상을 볼 수 있었고, 신경회로망에 더하여 성능을 개선시키기 위해 신경회로망을 보완 할 수 있는 다수결 투표 방법을 사용하였다. 본 논문에서는 신경회로망의 인식 결과를 비교 분석하여 최적의 특징을 찾아 낸 결과를 2차 다수결 투표를 사용하여 인식하는 방법을 제안한다. 제안된 방식의 성능을 검증하기 위해서 Concorida 대학교의 CENPARIMI 숫자 데이터 베이스를 가지고 인식을 수행 하였으며. 그 결과 97.40%의 정인식률과 0.75%의 오인식률 그리고 1.85%의 거부률을 보였다.

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Adaptive controls for non-linear plant using neural network (신경회로망을 이용한 비선형 플랜트의 적응제어)

  • 정대원
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.215-218
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    • 1997
  • A dynamic back-propagation neural network is addressed for adaptive neural control system to approximate non-linear control system rather than static networks. It has the capability to represent the approximation of nonlinear system without mathematical analysis and to carry out the on-line learning algorithm for real time application. The simulated results show fast tracking capability and adaptive response by using dynamic back-propagation neurons.

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Constraints on Implementations of Neural Networks with Analog VLSI Circuits (신경 회로망의 아날로그 VLSI 구현시 나타나는 문제점)

  • Oh, S.H.;Lee, Y.
    • Electronics and Telecommunications Trends
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    • v.9 no.1
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    • pp.75-80
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    • 1994
  • 신경회로망을 아날로그 VLSI로 구현하는 것은 디지털 구현방법에 비하여 집적도와 신호처리 속도의 장점이 있는 반면에 아날로그 신호의 저장 방법, 시냅스를 구현한 곱셈기의 비선형성, 동작영역, zero offset, noise, gain의 변동등의 문제가 존재한다. 여기서는, 이러한 문제들이 신경회로망을 구현한 아날로그 회로에서 어떤 형태로 나타나는지 알아보았다. 위와 같은 비이상적 요인들이 신경회로망의 성능에 미치는 영향이 파악되면 보다 더 신뢰성을 갖는 신경회로망 chip을 설계/제작할 수 있을 것이다.