• Title/Summary/Keyword: neural networks

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Nonlinear Neural Networks for Vehicle Modeling Control Algorithm based on 7-Depth Sensor Measurements (7자유도 센서차량모델 제어를 위한 비선형신경망)

  • Kim, Jong-Man;Kim, Won-Sop;Sin, Dong-Yong
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • pp.525-526
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    • 2008
  • For measuring nonlinear Vehicle Modeling based on 7-Depth Sensor, the neural networks are proposed m adaptive and in realtime. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models.

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A Controlled Neural Networks of Nonlinear Modeling with Adaptive Construction in Various Conditions (다변 환경 적응형 비선형 모델링 제어 신경망)

  • Kim, Jong-Man;Sin, Dong-Yong
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • pp.1234-1238
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    • 2004
  • A Controlled neural networks are proposed in order to measure nonlinear environments in adaptive and in realtime. The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between tile output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we have various experiments. And this controller call prove effectively to be control in the environments of various systems.

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A Learning Algorithm of Fuzzy Neural Networks with Trapezoidal Fuzzy Weights

  • Lee, Kyu-Hee;Cho, Sung-Bae
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.404-409
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    • 1998
  • In this paper, we propose a learning algorithm of fuzzy neural networks with trapezoidal fuzzy weights. This fuzzy neural networks can use fuzzy numbers as well as real numbers, and represent linguistic information better than standard neural networks. We construct trapezodal fuzzy weights by the composition of two triangles, and devise a learning algorithm using the two triangular membership functions, The results of computer simulations on numerical data show that the fuzzy neural networks have high fitting ability for target output.

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The optimization of fuzzy neural network using genetic algorithms and its application to the prediction of the chaotic time series data (유전 알고리듬을 이용한 퍼지 신경망의 최적화 및 혼돈 시계열 데이터 예측에의 응용)

  • Jang, Wook;Kwon, Oh-Gook;Joo, Young-Hoon;Yoon, Tae-Sung;Park, Jin-Bae
    • 제어로봇시스템학회:학술대회논문집
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    • pp.708-711
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    • 1997
  • This paper proposes the hybrid algorithm for the optimization of the structure and parameters of the fuzzy neural networks by genetic algorithms (GA) to improve the behaviour and the design of fuzzy neural networks. Fuzzy neural networks have a distinguishing feature in that they can possess the advantage of both neural networks and fuzzy systems. In this way, we can bring the low-level learning and computational power of neural networks into fuzzy systems and also high-level, human like IF-THEN rule thinking and reasoning of fuzzy systems into neural networks. As a result, there are many research works concerning the optimization of the structure and parameters of fuzzy neural networks. In this paper, we propose the hybrid algorithm that can optimize both the structure and parameters of fuzzy neural networks. Numerical example is provided to show the advantages of the proposed method.

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Flood Stage Forecasting using Class Segregation Method of Time Series Data (시계열자료의 계층분리기법을 이용한 하천유역의 홍수위 예측)

  • Kim, Sung-Weon
    • 한국방재학회:학술대회논문집
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    • pp.669-673
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    • 2008
  • In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

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The development of semi-active suspension controller based on error self recurrent neural networks (오차 자기순환 신경회로망 기반 반능동 현가시스템 제어기 개발)

  • Lee, Chang-Goo;Song, Kwang-Hyun
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.8
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    • pp.932-940
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    • 1999
  • In this paper, a new neural networks and neural network based sliding mode controller are proposed. The new neural networks are an mor self-recurrent neural networks which use a recursive least squares method for the fast on-line leammg. The error self-recurrent neural networks converge considerably last than the back-prollagation algorithm and have advantage oi bemg less affected by the poor initial weights and learning rate. The controller for suspension system is designed according to sliding mode technique based on new proposed neural networks. In order to adapt shding mode control mnethod, each frame dstance hetween ground and vehcle body is estimated md controller is designed according to estimated neural model. The neural networks based sliding mode controller approves good peiformance throllgh computer sirnulations.

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Optimal Structure of Wavelet Neural Network Systems using Genetic Algorithm (유전 알고리즘 이용한 웨이블릿 신경회로망의 최적 구조 설계)

  • 이창민;서재용;진홍태
    • Journal of the Korean Institute of Intelligent Systems
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    • v.10 no.4
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    • pp.338-342
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    • 2000
  • In order to approximate a nonlinear function, wacelet neural networks combining wacelet theory and neural networks have been proposed as an alternative to conventional multi-layered neural networks. wacelet neural networks provide better approximating performance than conventional neural networks. In this paper, an effective method to construct an optimal wavelet neural network is proposed using genetic alogorithm. Genetic Algorithm is used to determine dilationa and translations of wavelet basic functions of wavelet neural networks. Then, these determined dilations dilations and translations, wavelet neural networks are funther trained by back propagation learning algorithm. The effectiveness of the final network is verified thrifigh the approximation result of a nonlinear function and comparison with conventional neural networks.

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A Estimated Neural Networks for Adaptive Cognition of Nonlinear Road Situations (굴곡있는 비선형 도로 노면의 최적 인식을 위한 평가 신경망)

  • Kim, Jong-Man;Kim, Young-Min;Hwang, Jong-Sun;Sin, Dong-Yong
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • pp.573-577
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    • 2002
  • A new estimated neural networks are proposed in order to measure nonlinear road environments in realtime. This new neural networks is Error Estimated Neural Networks. The structure of it is similar to recurrent neural networks; a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we control 7 degree simulation, this controller and driver were proved to be effective to drive a car in the environments of nonlinear road systems.

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Structural Design of Radial Basis Function-based Polynomial Neural Networks by Using Multiobjective Particle Swarm Optimization (다중 목적 입자 군집 최적화 알고리즘 이용한 방사형 기저 함수 기반 다항식 신경회로망 구조 설계)

  • Kim, Wook-Dong;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.1
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    • pp.135-142
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    • 2012
  • In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.

Tension Estimation of Tire using Neural Networks and DOE (신경회로망과 실험계획법을 이용한 타이어의 장력 추정)

  • Lee, Dong-Woo;Cho, Seok-Swoo
    • Journal of the Korean Society for Precision Engineering
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    • v.28 no.7
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    • pp.814-820
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    • 2011
  • It takes long time in numerical simulation because structural design for tire requires the nonlinear material property. Neural networks has been widely studied to engineering design to reduce numerical computation time. The numbers of hidden layer, hidden layer neuron and training data have been considered as the structural design variables of neural networks. In application of neural networks to optimize design, there are a few studies about arrangement method of input layer neurons. To investigate the effect of input layer neuron arrangement on neural networks, the variables of tire contour design and tension in bead area were assigned to inputs and output for neural networks respectively. Design variables arrangement in input layer were determined by main effect analysis. The number of hidden layer, the number of hidden layer neuron and the number of training data and so on have been considered as the structural design variables of neural networks. In application to optimization design problem of neural networks, there are few studies about arrangement method of input layer neurons. To investigate the effect of arrangement of input neurons on neural network learning tire contour design parameters and tension in bead area were assigned to neural input and output respectively. Design variables arrangement in input layer was determined by main effect analysis.