• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.175-180
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• 2012

A nonlinear navigation filter for biomimetic robot using analytic approximation of mean and covariance of state variable is proposed. The approximations are performed at the time update step in the filter structure. The mean is approximated to the 3rd order of Taylor's series expansion of true mean and the covariance is approximated to the 3rd order either. The famous EKF is a nonlinear filtering method approximating the mean to 1st order and the covariance to the 3rd order. The UKF approximate them to the higher orders by numerical method. The proposed method derived a analytical approximation of them for navigation system and therefore don't need so called sigma point transformation in UKF. The simulation results show that the proposed method can be a good alternative of UKF in the systems which require less computational burden.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.137-140
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• 1999

A lot of researcher have proposed a method of kernel identifying nonlinear system by use of Wiener kernels[6-7] or Volterra kernel[5] and so on. In this research, the authors proposed a method of identifying Volterra kernels for nonlinear system by use of pseudorandom M-sequence in which a crosscorrelation function between input and output of a nonlinear system is taken[4]. we can be applied to an MISO nonlinear system or a system which depends on its input amplitude[2]. But, there exist many systems in which it is difficult to determine a Volterra kernel having the same time coordinate on the crosscorrelation function. In those cases, we have to estimate Volterra kernel by using its neighboring points[4]. In this paper, we propose a new method for not estimating but obtaining Volterra kernel having the same time coordinate using calculation between the neighboring points. Some numerical simulations show that this method is effective for obtaining higher order Volterra kernel of nonlinear control systems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06a
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• pp.165-170
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• 1998

In this paper, the effect of nonlinear distortion, caused by a high-power amplifier (HPA) in an orthogonal frequency division multiplexing (OFDM) system, on the receiver part is analyzed. Since the HPA, which can be modeled by a memoryless Volterra system, distorts OFDM signals in a nonlinear fashion, the received signal at each subchannel includes the multiplicative distortion of 1-st order as well as additive nonlinear distortion of higher-order. The nonlinear distortion can be viewed as a nonlinear interchannel interference (NICI) since it consists of harmonic distortions and intermodulation distortions, produced by other subchannels affecting the subchannel of interest. In this paper, were analytically derive the variance of NICI in terms of average input power using the Volterra model for HPA, and then calculate the bit-error rate (BER) performance of an OFDM system. Also, we propose a simple method to compensate for the phase distortion in OFDM system amplified by HPA, and calculate its BER performance. Validity of the proposed approach is verified by computer simulations for an OFDM system employing 16-QAM constellation input.

• Steel and Composite Structures
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• v.19 no.4
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• pp.1011-1033
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• 2015

In this article, large amplitude bending behaviour of laminated composite flat panel under combined effect of moisture, temperature and mechanical loading is investigated. The laminated composite panel model has been developed mathematically by introducing the geometrical nonlinearity in Green-Lagrange sense in the framework of higher-order shear deformation theory. The present study includes the degraded composite material properties at elevated temperature and moisture concentration. In order to achieve any general case, all the nonlinear higher order terms have been included in the present formulation and the material property variations are introduced through the micromechanical model. The nonlinear governing equation is obtained using the variational principle and discretised using finite element steps. The convergence behaviour of the present numerical model has been checked. The present proposed model has been validated by comparing the responses with those available published results. Some new numerical examples have been solved to show the effect of various parameters on the bending behaviour of laminated composite flat panel under hygro-thermo-mechanical loading.

• Journal of applied mathematics & informatics
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• v.30 no.3_4
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• pp.395-411
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• 2012

Several methods with order higher than that of Newton methods which are of order 2 have been reported in literature for solving nonlinear equations. The focus of most of these methods was to economize on/minimize the number of function evaluations per iterations. We have demonstrated here that there are several computational pit-falls, such as the violation of fixed-point theorem, that one could encounter while using these methods. Further it was also shown that the overall computational complexity could be more in these high-order methods than that in the second-order Newton method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.1
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• pp.251-268
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• 1996

In this paper, a higher-order feedforward neural network called ridge polynomial network (RPN) which shows good approximation capability for nonlnear continuous functions defined on compact subsets in multi-dimensional Euclidean spaces, is presented. This network provides more efficient and regular structure as compared to ordinary higher-order feedforward networks based on Gabor-Kolmogrov polynomial expansions, while maintating their fast learning property. the ridge polynomial network is a generalization of the pi-sigma network (PSN) and uses a specialform of ridge polynomials. It is shown that any multivariate polynomial can be exactly represented in this form, and thus realized by a RPN. The approximation capability of the RPNs for arbitrary continuous functions is shown by this representation theorem and the classical weierstrass polynomial approximation theorem. The RPN provides a natural mechanism for incremental function approximation based on learning algorithm of the PSN. Simulation results on several applications such as multivariate function approximation and pattern classification assert nonlinear approximation capability of the RPN.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.375-379
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• 2000

In this paper higher order spectral techniques are applied to some simple mechanical systems. The system studied is the nonlinear magnetic beam. This is a simply supported beam, driven by an electromagnetic shaker. At the free end, pairs of repelling magnets are placed. By varying the position and number of magnets, the nature of the nonlinearity can be changed, be it skewed or symmetric, and by varying the distance between the magnets the strength of the nonlinearity can also be altered. Using this controllable system, auto higher order spectral methods are applied, assuming only a knowledge of an output signal.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.64-69
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• 2006

Due to the inherent nonlinear nature of Electro-rheological (ER) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the nonlinear damping force model is made to identify the properties of the ER damper using higher order spectrum. The higher order spectral analysis is used to investigate the nonlinear frequency coupling phenomena with the damping force signal according to the sinusoidal excitation of the damper. Also, this paper presents an inverse model of the ER damper, i.e., the model can predict the required voltage so that the ER damper can produce the desired force for the requirement of vibration control of vehicle suspension systems. The inverse model is constructed by using a multi-layer perceptron neural network. A quarter-car suspension model is considered in this paper for analysis and simulation. Simulation results show that the proposed inverse model of ER damper can obtain control voltage of ER damper for required damping force.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.135-151
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• 2022

The generalized displacement method is a nonlinear solution scheme that follows the equilibrium path of the structure based on the development of the generalized displacement. This method traces the path uniformly with a constant amount of generalized displacement. In this article, we first develop higher-order generalized displacement methods based on multi-point techniques. According to the concept of generalized stiffness, a relation is proposed to adjust the generalized displacement during the path-following. This formulation provides the possibility to change the amount of generalized displacement along the path due to changes in generalized stiffness. We, then, introduce higher-order algorithms of variable generalized displacement method using multi-point methods. Finally, we demonstrate with numerical examples that the presented algorithms, including multi-point generalized displacement methods and multi-point variable generalized displacement methods, are capable of following the equilibrium path. A comparison with the arc length method, generalized displacement method, and multi-point arc-length methods illustrates that the adjustment of generalized displacement significantly reduces the number of steps during the path-following. We also demonstrate that the application of multi-point methods reduces the number of iterations.

• Communications of the Korean Mathematical Society
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• v.33 no.2
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• pp.677-693
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• 2018

In this work, we generalize a family of optimal eighth order weighted-Newton methods to Banach spaces and study its local convergence to approximate a locally-unique solution of a system of nonlinear equations. The convergence in this study is shown under hypotheses only on the first derivative. Our analysis avoids the usual Taylor expansions requiring higher order derivatives but uses generalized Lipschitz-type conditions only on the first derivative. Moreover, our new approach provides computable radius of convergence as well as error bounds on the distances involved and estimates on the uniqueness of the solution based on some functions appearing in these generalized conditions. Such estimates are not provided in the approaches using Taylor expansions of higher order derivatives which may not exist or may be very expensive or impossible to compute. The convergence order is computed using computational order of convergence or approximate computational order of convergence which do not require usage of higher derivatives. This technique can be applied to any iterative method using Taylor expansions involving high order derivatives. The study of the local convergence based on Lipschitz constants is important because it provides the degree of difficulty for choosing initial points. In this sense the applicability of the method is expanded. Finally, numerical examples are provided to verify the theoretical results and to show the convergence behavior.