• 제목/요약/키워드: neural network(NN)

검색결과 368건 처리시간 0.029초

A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control

  • Lin, Chih-Hong
    • Journal of Power Electronics
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    • 제14권5호
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    • pp.1008-1027
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    • 2014
  • An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

유전알고리즘과 신경회로망을 이용한 선형유도전동기의 최적설계 (Optimum Design of a linear Induction Motor using Genetic Algorithm and Neural Network)

  • 김창업
    • 조명전기설비학회논문지
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    • 제17권5호
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    • pp.29-35
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    • 2003
  • 본 논문에서는 유전 알고리즘과 신경 회로망을 이용하여 선형유도전동기의 최적화 설계 방법에 대하여 연구하였다. 최대 추력 및 추력/중량을 목적함수로 하여 유전알고리즘, 신경회로망, 유전알고리즘과 신경회로망의 합성에 의한 방법으로 선형유도전동기의 최적설계를 한 결과 제안한 방법이 가장 우수함을 확인하였다.

Neural Networks and Logistic Models for Classification: A Case Study

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
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    • 제7권1호
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    • pp.13-19
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    • 1996
  • In this paper, we study and compare two types of methods for classification when both continuous and categorical variables are used to describe each individual. One is neural network(NN) method using backpropagation learning(BPL). The other is logistic model(LM) method. Both the NN and LM are based on projections of the data in directions determined from interconnection weights.

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

  • Urasaki Naomitsu;Senjyu Tomonobu
    • Journal of Power Electronics
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    • 제6권4호
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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.

Speeding Up Neural Network-Based Face Detection Using Swarm Search

  • Sugisaka, Masanori;Fan, Xinjian
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2004년도 ICCAS
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    • pp.1334-1337
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    • 2004
  • This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to solve it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. To achieve better performance, the influence of PSO parameter settings on the search performance was investigated. Experiments show that with fine-adjusted parameters, the proposed method leads to a speedup of 94 on 320${\times}$240 images compared to the traditional exhaustive search method.

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An Improved Genetic Algorithm for Fast Face Detection Using Neural Network as Classifier

  • Sugisaka, Masanori;Fan, Xinjian
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2005년도 ICCAS
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    • pp.1034-1038
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    • 2005
  • This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and develops an improved genetic algorithm (IGA) to solve it. Each individual in the IGA represents a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experimental results show that the proposed method leads to a speedup of 83 on $320{\times}240$ images compared to the traditional exhaustive search method.

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유도전동기의 속도 센서리스 제어를 위한 신경회로망 알고리즘의 추정 특성 비교 (Comparison of Different Schemes for Speed Sensorless Control of Induction Motor Drives by Neural Network)

  • 이경훈;국윤상;김윤호;최원범
    • 전력전자학회:학술대회논문집
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    • 전력전자학회 1999년도 전력전자학술대회 논문집
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    • pp.526-530
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    • 1999
  • This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.

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자코비안을 이용한 최적의 신경망 제어기 설계 (Optimal Neural Network Controller Design using Jacobian)

  • 임윤규;정병묵;조지승
    • 한국정밀공학회지
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    • 제20권2호
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    • pp.85-93
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    • 2003
  • Generally, it is very difficult to get a modeling equation because multi-variable system has coupling relations between its inputs and outputs. To design an optimal controller without the modeling equation, this paper proposes a neural-network (NN) controller being learned by Jacobian matrix. Another major characteristic is that the controller consists of two separated NN controllers, namely, proportional control part and derivative control part. Simulation results for a catamaran system show that the proposed NN controller is superior to LQR in the regulation and tracking problems.

A neural network model to assess the hysteretic energy demand in steel moment resisting frames

  • Akbas, Bulent
    • Structural Engineering and Mechanics
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    • 제23권2호
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    • pp.177-193
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    • 2006
  • Determining the hysteretic energy demand and dissipation capacity and level of damage of the structure to a predefined earthquake ground motion is a highly non-linear problem and is one of the questions involved in predicting the structure's response for low-performance levels (life safe, near collapse, collapse) in performance-based earthquake resistant design. Neural Network (NN) analysis offers an alternative approach for investigation of non-linear relationships in engineering problems. The results of NN yield a more realistic and accurate prediction. A NN model can help the engineer to predict the seismic performance of the structure and to design the structural elements, even when there is not adequate information at the early stages of the design process. The principal aim of this study is to develop and test multi-layered feedforward NNs trained with the back-propagation algorithm to model the non-linear relationship between the structural and ground motion parameters and the hysteretic energy demand in steel moment resisting frames. The approach adapted in this study was shown to be capable of providing accurate estimates of hysteretic energy demand by using the six design parameters.

Neural network based model for seismic assessment of existing RC buildings

  • Caglar, Naci;Garip, Zehra Sule
    • Computers and Concrete
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    • 제12권2호
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    • pp.229-241
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    • 2013
  • The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.