• Title, Summary, Keyword: Neural Network Modeling

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A Robust Nonlinear Control Using the Neural Network Model on System Uncertainty (시스템의 불확실성에 대한 신경망 모델을 통한 강인한 비선형 제어)

  • 이수영;정명진
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.43 no.5
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    • pp.838-847
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    • 1994
  • Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

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Analysis of Neural Network Approaches for Nonlinear Modeling of Switched Reluctance Motor Drive

  • Saravanan, P;Balaji, M;Balaji, Nagaraj K;Arumugam, R
    • Journal of Electrical Engineering and Technology
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    • v.12 no.4
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    • pp.1548-1555
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    • 2017
  • This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition to conventional neural network are explored for improving the modeling accuracy of SRM. The neural structures are trained with the data obtained by modeling of SRM using Finite Element Analysis (FEA) and the trained neural network is incorporated in the model of SRM drive. The results signify the modeling accuracy with GRNN model. The closed loop drive simulation is performed in MATLAB/Simulink environment and the closeness of the results in comparison with the experimental prototype validates the modeling approach.

Comparative Study on Surrogate Modeling Methods for Rapid Electromagnetic Forming Analysis

  • Lee, Seungmin;Kang, Beom-Soo;Lee, Kyunghoon
    • Transactions of Materials Processing
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    • v.27 no.1
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    • pp.28-36
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    • 2018
  • Electromagnetic forming is a type of high-speed forming process to deform a workpiece through a Lorentz force. As the high strain rate in an electromagnetic-forming simulation causes infeasibility in determining constitutive parameters, we employed inverse parameter estimation in the previous study. However, the inverse parameter estimation process required us to spend considerable time, which leads to an increase in computational cost. To overcome the computational obstacle, in this research, we applied two types of surrogate modeling methods and compared them to each other to evaluate which model is best for the electromagnetic-forming simulation. We exploited an artificial neural network and we reduced-order modeling methods. During the construction of a reduced-order model, we extracted orthogonal bases with proper orthogonal decomposition and predicted basis coefficients by utilizing an artificial neural network. After the construction of the surrogate models, we verified the artificial neural network and reduced-order models through training and testing samples. As a result, we determined the artificial neural network model is slightly more accurate than the reduced-order model. However, the construction of the artificial neural network model requires a considerably larger amount of time than that of the reduced-order model. Thus, a reduced order modeling method is more efficient than an artificial neural network for estimating the electromagnetic forming and for the rapid approximation of structural simulations which needs repetitive runs.

The Modeling of Chaotic Nonlinear System Using Wavelet Based Fuzzy Neural Network

  • Oh, Joon-Seop;You, Sung-Jin;Park, Jin-Bae;Choi, Yoon-Ho
    • 제어로봇시스템학회:학술대회논문집
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    • pp.635-639
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    • 2004
  • In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

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Modeling of Nuclear Power Plant Steam Generator using Neural Networks (신경회로망을 이용한 원자력발전소 증기발생기의 모델링)

  • 이재기;최진영
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.4
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    • pp.551-560
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    • 1998
  • This paper presents a neural network model representing complex hydro-thermo-dynamic characteristics of a steam generator in nuclear power plants. The key modeling processes include training data gathering process, analysis of system dynamics and determining of the neural network structure, training process, and the final process for validation of the trained model. In this paper, we suggest a training data gathering method from an unstable steam generator so that the data sufficiently represent the dynamic characteristics of the plant over a wide operating range. In addition, we define the inputs and outputs of neural network model by analyzing the system dimension, relative degree, and inputs/outputs of the plant. Several types of neural networks are applied to the modeling and training process. The trained networks are verified by using a class of test data, and their performances are discussed.

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Predictive Modeling of Competitive Biosorption Equilibrium Data

  • Chu K.H.;Kim E.Y.
    • Biotechnology and Bioprocess Engineering:BBE
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    • v.11 no.1
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    • pp.67-71
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    • 2006
  • This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

Application of a Hybrid System of Probabilistic Neural Networks and Artificial Bee Colony Algorithm for Prediction of Brand Share in the Market

  • Shahrabi, Jamal;Khameneh, Sara Mottaghi
    • Industrial Engineering and Management Systems
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    • v.15 no.4
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    • pp.324-334
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    • 2016
  • Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

A Study on the Nonlinear Modeling of Base Isolator Systems by a Neural Network Theory : Application to Lead Rubber Bearings (신경망 이론을 이용한 지진격리 장치의 비선형 모델링 기법 연구 : 납삽입 적층 고무베어링에 적용한 예)

  • 허영철;김영중;김병현
    • Proceedings of the Earthquake Engineering Society of Korea Conference
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    • pp.433-441
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    • 2003
  • In this paper, a study on the nonlinear modeling of lead rubber bearings(LRBs) by a neural network theory was carried out. The random tests on the LRB were used for a training of neural network model. Numerical simulations using the neural network model were peformed on a scaled structural model with the LRBs excited by three type of seismic loads and compared with the shaking table tests. As a result, it was shown that the neural network model would be useful to a numerical modeling of LRB.

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Three Dimensional Environment Modeling for Mobile Robots Using Growing Neural Gas Network

  • Kim, Min-Young;Cho, Hyung-Suck;Kim, Jae-Hoon
    • 제어로봇시스템학회:학술대회논문집
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    • pp.30.2-30
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    • 2001
  • As the era of the human friendly robot looms, the intelligent autonomous mobile robots have obtained tremendous interests in recent years. The robots may be service robots for serving human or industrial robots for replacing human. For the coexistance with human, the robots must be able to feel and recognize three dimensional space that human live. In this paper, we propose three dimensional environmental modeling method based on a neural network technique called Growing Neural Gas Network. The purpose of this neural network is to generate a graphical structure which reflects the topology of the input space. Through this method, the robots´ surroundings are autonomously segmented ...

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Neural-based Blind Modeling of Mini-mill ASC Crown

  • Lee, Gang-Hwa;Lee, Dong-Il;Lee, Seung-Joon;Lee, Suk-Gyu;Kim, Shin-Il;Park, Hae-Doo;Park, Seung-Gap
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.6
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    • pp.577-582
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    • 2002
  • Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.