• Title/Summary/Keyword: Neural network(NN)

Search Result 368, Processing Time 0.045 seconds

Neural network heterogeneous autoregressive models for realized volatility

  • Kim, Jaiyool;Baek, Changryong
    • Communications for Statistical Applications and Methods
    • /
    • v.25 no.6
    • /
    • pp.659-671
    • /
    • 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
    • /
    • 2009.03a
    • /
    • pp.456-463
    • /
    • 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.

  • PDF

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
    • /
    • v.10 no.12
    • /
    • pp.1249-1255
    • /
    • 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.

Improved BP-NN Controller of PMSM for Speed Regulation

  • Feng, Li-Jia;Joung, Gyu-Bum
    • International journal of advanced smart convergence
    • /
    • v.10 no.2
    • /
    • pp.175-186
    • /
    • 2021
  • We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.

Neural network controller design with a performance evaluation level (성능평가 계층을 가지는 신경망제어기 설계)

  • 이현철;조원철;전기준
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1992.10a
    • /
    • pp.613-618
    • /
    • 1992
  • We propose a new control architecture which consists of a PI controller and a neural network(NN) controller connected together in parallel. This architecture is well adapted to a wide range of uncertainties and variations of systems. The NN controller is learned through weights of the emulator which identify the dynamic chracteristics of the systems. A performance evaluation level of two NN's decides automatically which controller of the two controllers will be used mainly. The PI controller operates mainly during learning phase of the NN controller whereas a good performance is obtained from the NN controller only, when the NN controller is learned sufficiently.

  • PDF

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
    • /
    • v.16 no.4
    • /
    • pp.259-267
    • /
    • 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
    • /
    • v.1 no.2
    • /
    • pp.103-121
    • /
    • 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.

  • PDF

Development of Neural Network Controller for Maximum Power Point Tracking of PV System (PV 시스템의 최대전력점 추적을 위한 신경회로망 제어기 개발)

  • Ko, Jae-Sub;Choi, Jung-Sik;Jung, Dong-Hwa
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
    • /
    • v.23 no.1
    • /
    • pp.41-48
    • /
    • 2009
  • This paper presents an Neural Network(NN) controller for Maximum Power Point Tracking (MPPT) of PV supplied DC motor. A variation of solar irradiation is most important factor in the MPPT of PV system. That is nonlinear, aperiodic and complicated. NN was widely used due to easily solving a complex math problem. Proposed photovoltaic system consists of NN, DC-DC converter, DC motor and load(cf, pump). NN algorithm apply to DC-DC converter through an Adaptive control of Neural Network, calculates Converter-Chopping ratio using an Adaptive control of NN. The results of an Adaptive control of NN compared with the results of Converter-Chopping ratio which are calculated mathematical modeling and evaluate the proposed algorithm. The experimental data show that an adequacy of the algorithm was established through the compared data.

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
    • /
    • 2000.11b
    • /
    • pp.124-131
    • /
    • 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.

  • PDF

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
    • /
    • v.68 no.1
    • /
    • pp.153-158
    • /
    • 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.