• East Asian mathematical journal
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• v.34 no.3
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• pp.295-303
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• 2018

Gautam et al. [9] introduced the multivariable A-function, which is very general, reduces to yield a number of special functions, in particular, the multivariable H-function. Here, first, we aim to establish two very general integral formulas involving product of the general class of Srivastava multivariable polynomials and the multivariable A-function. Then, using those integrals, we find a solution of partial differential equations of heat conduction at zero temperature with radiation at the ends in medium without source of thermal energy. The results presented here, being very general, are also pointed out to yield a number of relatively simple results, one of which is demonstrated to be connected with a known solution of the above-mentioned equation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.21-28
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• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.240-245
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• 2012

A 160 MW drum-type boiler-turbine system is developed in the present research through a multi-multivariable dynamic matrix control (DMC) scheme and a multi-multivariable model approach. A novel intelligence-based decision mechanism (IBDM) is realized to support both model approach and control scheme. In such case, the responsibility of the proposed IBDM is to identify the best multivariable model of the system and the corresponding multivariable DMC scheme to cope with the system at each instant of time in an appropriate manner.

• Kyungpook Mathematical Journal
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• v.47 no.4
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• pp.495-500
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• 2007

The aim of this paper is to derive a fractional derivative of the multivariable H-function of Srivastava and Panda [7], associated with a general class of multivariable polynomials of Srivastava [4] and the generalized Lauricella functions of Srivastava and Daoust [9]. Certain special cases have also been discussed. The results derived here are of a very general nature and hence encompass several cases of interest hitherto scattered in the literature.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.238-243
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• 1986

Distillation columns are widely used in almost every chemical plant. The use of multivariable control for such units is attractive because of the strong interactions exhibited between outputs and inputs and the desire to control simultaneously both top and bottom products. In this research design of a robust multivariable controller for distillation column was considered; output feedback controller with proportional and integral modes was designed using pole assignment. The transfer function matrix was obtained by fitting the step response realtions between single input double output pairs of variables. This matrix was then converted to linear time invariant state space model by multivariable realization technique. With the proposed multivariable proportional and integral controller applied to the process, the result of the digital computer simulation showed a good performance of asymtotic tracking. The limited experimental performance of this multivariable control was compared with the result from simulation. It was found that the proposed controller performed satisfactorily for the distillation column which separated binary mixture of methanol and water.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.710-713
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• 1996

This paper deals with the fault detection problem in uncertain linear multivariable systems having both model mismatch and noise. A robust detection presented by Kwon et al.(1994) for SISO systems has been here extended to the multivariable systems are derived. The model mismatch includes here linearization error as well as undermodelling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.

• Journal of the Korean Society of Industry Convergence
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• v.9 no.3
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• pp.173-178
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• 2006

For the design of multivariable fuzzy control systems decomposition of control rules is a efficent inference method since it alleviates the complexity of the problem. In some systems, however, inference error of the Gupta's decomposition method is inevitable because of its approximate nature. In this paper we define indices of applicability which decides whether the decomposition method can be applied to a multivariable fuzzy system or not.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.433-440
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• 2001

In this paper, we propose an automatic navigation system of ship using multivariable fuzzy control system in dynamic sea environment. The proposed multivariable fuzzy control system consists of two subsystems with three inputs and two outputs. The effectiveness of the proposed multivariable fuzzy control system is shown by simulation results.

• Structural Engineering and Mechanics
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• v.50 no.5
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• pp.679-695
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• 2014

A design method of second generation wavelet (SGW)-based multivariable finite elements is proposed for static and vibration beam analysis. An important property of SGWs is that they can be custom designed by selecting appropriate lifting coefficients depending on the application. The SGW-based multivariable finite element equations of static and vibration analysis of beam problems with two and three kinds of variables are derived based on the generalized variational principles. Compared to classical finite element method (FEM), the second generation wavelet-based multivariable finite element method (SGW-MFEM) combines the advantages of high approximation performance of the SGW method and independent solution of field functions of the MFEM. A multiscale algorithm for SGW-MFEM is presented to solve structural engineering problems. Numerical examples demonstrate the proposed method is a flexible and accurate method in static and vibration beam analysis.