• Title, Summary, Keyword: Neural Network (NN)

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Neural network heterogeneous autoregressive models for realized volatility

  • Kim, Jaiyool;Baek, Changryong
    • Communications for Statistical Applications and Methods
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    • v.25 no.6
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    • pp.659-671
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    • 2018
  • In this study, we consider the extension of the heterogeneous autoregressive (HAR) model for realized volatility by incorporating a neural network (NN) structure. Since HAR is a linear model, we expect that adding a neural network term would explain the delicate nonlinearity of the realized volatility. Three neural network-based HAR models, namely HAR-NN, $HAR({\infty})-NN$, and HAR-AR(22)-NN are considered with performance measured by evaluating out-of-sample forecasting errors. The results of the study show that HAR-NN provides a slightly wider interval than traditional HAR as well as shows more peaks and valleys on the turning points. It implies that the HAR-NN model can capture sharper changes due to higher volatility than the traditional HAR model. The HAR-NN model for prediction interval is therefore recommended to account for higher volatility in the stock market. An empirical analysis on the multinational realized volatility of stock indexes shows that the HAR-NN that adds daily, weekly, and monthly volatility averages to the neural network model exhibits the best performance.

Improvement of Initial Weight Dependency of the Neural Network Model for Determination of Preconsolidation Pressure from Piezocone Test Result (피에조콘을 이용한 선행압밀하중 결정 신경망 모델의 초기 연결강도 의존성 개선)

  • Park, Sol-Ji;Joo, No-Ah;Park, Hyun-Il;Kim, Young-Sang
    • Proceedings of the Korean Geotechical Society Conference
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    • pp.456-463
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    • 2009
  • The preconsolidation pressure has been commonly determined by oedometer test. However, it can also be determined by in-situ test, such as piezocone test with theoretical and(or) empirical correlations. Recently, Neural Network(NN) theory was applied and some models were proposed to estimate the preconsolidation pressure or OCR. However, since the optimization process of synaptic weights of NN model is dependent on the initial synaptic weights, NN models which are trained with different initial weights can't avoid the variability on prediction result for new database even though they have same structure and use same transfer function. In this study, Committee Neural Network(CNN) model is proposed to improve the initial weight dependency of multi-layered neural network model on the prediction of preconsolidation pressure of soft clay from piezocone test result. It was found that even though the NN model has the optimized structure for given training data set, it still has the initial weight dependency, while the proposed CNN model can improve the initial weight dependency of the NN model and provide a consistent and precise inference result than existing NN models.

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A Performance Improvement for Tracking Controller of a Mobile Robot Using Neural Networks (신경망을 이용한 이동로봇 궤적제어기 성능개선)

  • Park Jae-Hwae;Lee Man-Hyung;Lee JangMyung
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.12
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    • pp.1249-1255
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    • 2004
  • A new parameter adaptation scheme for RBF Neural Network (NN) has been developed in this paper. Even though the RBF Neural Network (NN) based controllers are robust against both un-modeled dynamics and external disturbances, the performance is not satisfactory for a fast and precise mobile robot. To improve the tracking performance as well as robustness, all the parameters of RBF NN are updated in real time. The stability of this control law is rigorously proved by following the Lyapunov stability theory and shown by the experimental simulations. The fact that all of the weighting factors, width and center of RBF NN have been updated implies that this scheme utilizes all the possibilities in RBF NN to make the controller robust and precise while the mobile robot is following un-known trajectories. The performance of this new algorithm has been compared to the conventional RBF NN controller where some of the parameters are adjusted for robustness.

High Efficiency Drive Technique for Synchronous Reluctance Motors Using a Neural Network

  • Urasaki Naomitsu;Senjyu Tomonobu
    • Journal of Power Electronics
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    • v.6 no.4
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    • pp.340-346
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    • 2006
  • A high efficiency drive technique for synchronous reluctance motors (SynRM) using a neural network (NN) is presented in this paper. High efficiency drive condition depends on the mathematical model of SynRM. A NN is employed as an adaptive model of SynRM. The proposed high efficiency drive technique does not require an accurate mathematical model of SynRM. Moreover, the proposed method shows robustness against machine parameter variations because the training algorithm of the NN is executed on-line. The usefulness of the proposed method is confirmed through experimentation.

Regional Extension of the Neural Network Model for Storm Surge Prediction Using Cluster Analysis (군집분석을 이용한 국지해일모델 지역확장)

  • Lee, Da-Un;Seo, Jang-Won;Youn, Yong-Hoon
    • Atmosphere
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    • v.16 no.4
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    • pp.259-267
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    • 2006
  • In the present study, the neural network (NN) model with cluster analysis method was developed to predict storm surge in the whole Korean coastal regions with special focuses on the regional extension. The model used in this study is NN model for each cluster (CL-NN) with the cluster analysis. In order to find the optimal clustering of the stations, agglomerative method among hierarchical clustering methods was used. Various stations were clustered each other according to the centroid-linkage criterion and the cluster analysis should stop when the distances between merged groups exceed any criterion. Finally the CL-NN can be constructed for predicting storm surge in the cluster regions. To validate model results, predicted sea level value from CL-NN model was compared with that of conventional harmonic analysis (HA) and of the NN model in each region. The forecast values from NN and CL-NN models show more accuracy with observed data than that of HA. Especially the statistics analysis such as RMSE and correlation coefficient shows little differences between CL-NN and NN model results. These results show that cluster analysis and CL-NN model can be applied in the regional storm surge prediction and developed forecast system.

Predicting Korea Composite Stock Price Index Movement Using Artificial Neural Network (인공신경망을 이용한 한국 종합주가지수의 방향성 예측)

  • 박종엽;한인구
    • Journal of Intelligence and Information Systems
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    • v.1 no.2
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    • pp.103-121
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    • 1995
  • This study proposes a artificial neural network method to predict the time to buy and sell the stocks listed on the Korea Composite Stock Price Index(KOSPI). Four types (NN1, NN2, NN3, NN4) of independent networks were developed to predict KOSPIs up/down direction after four weeks. These networks have a difference only in the length of learning period. NN5 - arithmetic average of four networks outputs - shows an higher accuracy than other network types and Multiple Linear Regression (MLR), and buying and selling simulation using systems outputs produces higher reture than buy-and-hold strategy.

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Feature Selecting and Classifying Integrated Neural Network Algorithm for Multi-variate Classification (다변량 데이터의 분류 성능 향상을 위한 특질 추출 및 분류 기법을 통합한 신경망 알고리즘)

  • Yoon, Hyun-Soo;Baek, Jun-Geol
    • IE interfaces
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    • v.24 no.2
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    • pp.97-104
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    • 2011
  • Research for multi-variate classification has been studied through two kinds of procedures which are feature selection and classification. Feature Selection techniques have been applied to select important features and the other one has improved classification performances through classifier applications. In general, each technique has been independently studied, however consideration of the interaction between both procedures has not been widely explored which leads to a degraded performance. In this paper, through integrating these two procedures, classification performance can be improved. The proposed model takes advantage of KBANN (Knowledge-Based Artificial Neural Network) which uses prior knowledge to learn NN (Neural Network) as training information. Each NN learns characteristics of the Feature Selection and Classification techniques as training sets. The integrated NN can be learned again to modify features appropriately and enhance classification performance. This innovative technique is called ALBNN (Algorithm Learning-Based Neural Network). The experiments' results show improved performance in various classification problems.

STUDY ON APPLICATION OF NEURO-COMPUTER TO NONLINEAR FACTORS FOR TRAVEL OF AGRICULTURAL CRAWLER VEHICLES

  • Inaba, S.;Takase, A.;Inoue, E.;Yada, K.;Hashiguchi, K.
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • pp.124-131
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    • 2000
  • In this study, the NEURAL NETWORK (hereinafter referred to as NN) was applied to control of the nonlinear factors for turning movement of the crawler vehicle and experiment was carried out using a small model of crawler vehicle in order to inspect an application of NN. Furthermore, CHAOS NEURAL NETWORK (hereinafter referred to as CNN) was also applied to this control so as to compare with conventional NN. CNN is especially effective for plane in many variables with local minimum which conventional NN is apt to fall into, and it is relatively useful to nonlinear factors. Experiment of turning on the slope of crawler vehicle was performed in order to estimate an adaptability of nonlinear problems by NN and CNN. The inclination angles of the road surface which the vehicles travel on, were respectively 4deg, 8deg, 12deg. These field conditions were selected by the object for changing nonlinear magnitude in turning phenomenon of vehicle. Learning of NN and CNN was carried out by referring to positioning data obtained from measurement at every 15deg in turning. After learning, the sampling data at every 15deg were interpolated based on the constructed learning system of NN and CNN. Learning and simulation programs of NN and CNN were made by C language ("Association of research for algorithm of calculating machine (1992)"). As a result, conventional NN and CNN were available for interpolation of sampling data. Moreover, when nonlinear intensity is not so large under the field condition of small slope, interpolation performance of CNN was a little not so better than NN. However, when nonlinear intensity is large under the field condition of large slope, interpolation performance of CNN was relatively better than NN.

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A Study of CR-DuNN based on the LSTM and Du-CNN to Predict Infrared Target Feature and Classify Targets from the Clutters (LSTM 신경망과 Du-CNN을 융합한 적외선 방사특성 예측 및 표적과 클러터 구분을 위한 CR-DuNN 알고리듬 연구)

  • Lee, Ju-Young
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.68 no.1
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    • pp.153-158
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    • 2019
  • In this paper, we analyze the infrared feature for the small coast targets according to the surrounding environment for autonomous flight device equipped with an infrared imaging sensor and we propose Cross Duality of Neural Network (CR-DuNN) method which can classify the target and clutter in coastal environment. In coastal environment, there are various property according to diverse change of air temperature, sea temperature, deferent seasons. And small coast target have various infrared feature according to diverse change of environment. In this various environment, it is very important thing that we analyze and classify targets from the clutters to improve target detection accuracy. Thus, we propose infrared feature learning algorithm through LSTM neural network and also propose CR-DuNN algorithm that integrate LSTM prediction network with Du-CNN classification network to classify targets from the clutters.

Corporate Credit Rating using Partitioned Neural Network and Case- Based Reasoning (신경망 분리모형과 사례기반추론을 이용한 기업 신용 평가)

  • Kim, David;Han, In-Goo;Min, Sung-Hwan
    • Journal of Information Technology Applications and Management
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    • v.14 no.2
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    • pp.151-168
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    • 2007
  • The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

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