• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2949-2952
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• 2000

A new neural networks and learning algorithm are proposed in order to measure nonlinear heights of complexed road environments in realtime without pre-information. This new neural networks is Error Self Recurrent Neural Networks(ESRN), 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 back-propagation 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 ESRN and learning algorithm and control nonlinear models. To show the performance of this one. we control 7 degree of freedom full car model with several control method. From this simulation. this estimation and controller were proved to be effective to the measurements of nonlinear road environment systems.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.2
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• pp.54-61
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• 2000

This paper presents the performance evaluation of channel estimation algorithm using RLS algorithm lot W-CDMA reverse link over Rayleigh fading channels. By obtaining BER(Bit Error Rate) performance through computer simulations, the RLS(Recursive Least Square) algorithm is compared with the existing WMSA(Weighted Averaging)(K=1,3) and constant gain algorithm. The channel structure, modulation and pilot patterns are applied to the ARIB (Association of Radio Industries and Business) and 3GPP (3rd Generation Partnership Project) ITU-R proposal for the IMT-2000. The BER performance of RLS algorithm with linear interpolation is similar to that of WMSA(K=1) and slightly superior to that of constant gain algorithm at low Doppler frequencies. Also, RLS algorithm performance is better than that of the WMSA(K=1,3) and constant gain algorithms about 4dB at BER=2.0$\times$$10^{-2}$ and Doppler frequencies $F_D$=320Hz. With increasing Doppler frequency, therefore, the BER performance of RLS algorithm with linear interpolation is superior to WMSA(K=L.3) and constant rain algorithms.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.4
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• pp.264-274
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• 2003

This paper presents a direct generalized minimum-variance self tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior, noises and time delays. The self-tuning controller with a PID structure is a combination of the simple structure of a PID controller and the characteristics of a self-tuning controller that can adapt to changes in the environment. The self-tuning control effect is achieved through the RLS (recursive least square) algorithm at the parameter estimation stage as well as through the Robbins-Monro algorithm at the stage of optimizing the design parameter of the controller. The neural network control effect which compensates for nonlinear factor is obtained from the learning algorithm which the learning error between the filtered reference and the auxiliary output of plant becomes zero. Computer simulation has shown that the proposed method works effectively on the nonlinear nonminimum phase system with time delays and changed system parameter.