• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.5
• /
• pp.98-106
• /
• 1998

This paper proposes an efficient learning algorithm for improving the training performance of the neural network. The proposed method improves the training performance by applying the backpropagation algorithm of a global optimization method which is a hybrid of a stochastic approximation and a conjugate gradient method. The approximate initial point for f a ~gtl obal optimization is estimated first by applying the stochastic approximation, and then the conjugate gradient method, which is the fast gradient descent method, is applied for a high speed optimization. The proposed method has been applied to the parity checking and the pattern classification, and the simulation results show that the performance of the proposed method is superior to those of the conventional backpropagation and the backpropagation algorithm which is a hyhrid of the stochastic approximation and steepest descent method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.1
• /
• pp.12-18
• /
• 2011

In this paper, we proposed Interval Type-2 polynomial Radial Basis Function Neural Networks. In the receptive filed of hidden layer, Interval Type-2 fuzzy set is used. The characteristic of Interval Type-2 fuzzy set has Footprint Of Uncertainly(FOU), which denotes a certain level of robustness in the presence of un-known information when compared with the type-1 fuzzy set. In order to improve the performance of proposed model, we used the linear polynomial function as connection weight of network. The parameters such as center values of receptive field, constant deviation, and connection weight between hidden layer and output layer are optimized by Conjugate Gradient Method(CGM) and Space Search Evolutionary Algorithm(SSEA). The proposed model is applied to gas furnace dataset and its result are compared with those reported in the previous studies.

• Journal of applied mathematics & informatics
• /
• v.5 no.3
• /
• pp.701-716
• /
• 1998

In this paper we develop algorithms in programming lan-guage SCHEME for implementation of the main first order gradient techniques for unconstrained optimization. Implementation of the de-scent techniques which use non-optimal descent steps as well as imple-mentation of the optimal descent techniques are described. Also we investigate implementation of the global problem called optimization along a line. Developed programs are effective and simpler with re-spect to the corresponding in the procedural programming languages. Several numerical examples are reported.

• Journal of Electrical Engineering and Technology
• /
• v.4 no.3
• /
• pp.365-369
• /
• 2009

Identification is considered to be among the main applications of inverse theory and its objective for a given physical system is to use data which is easily observable, to infer some of the geometric parameters which are not directly observable. In this paper, a parameter identification method using inverse problem methodology is proposed. The minimisation of the objective function with respect to the desired vector of design parameters is the most important procedure in solving the inverse problem. The conjugate gradient method is used to determine the unknown parameters, and Tikhonov's regularization method is then used to replace the original ill-posed problem with a well-posed problem. The simulation and experimental results are presented and compared.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.27 no.1
• /
• pp.61-68
• /
• 2003

This study deals with the use of the conjugate gradient method for the simultaneous estimation of two unknown boundary heat fluxes on the slab in reheating furnace. Temperature measurements by the experiment are used in the inverse analysis. The heat flux estimations for three different cases of measurement locations in the slab are performed: non-skid, skid, and shift-skid zones. The estimated heat fluxes for three cases indicated the three regions having local peak values of heat fluxes. The estimated temperatures at measurement locations were in good agreements with the measured temperatures within 5% relative error.

• Journal of computational fluids engineering
• /
• v.3 no.1
• /
• pp.37-45
• /
• 1998

A new and efficient inverse design method based on the numerical optimization technique has been developed. The 2-D incompressible Navier-Stokes equations are solved for obtaining the objective functions and coupled with the optimization procedure to perform the inverse design. The steepest descent and the conjugate gradient method have been applied to find the searching direction. The golden section method was applied to compute the design variable intervals. It has been found that the airfoil and the pump impellers are well converged to their targeting shapes.

• Computers and Concrete
• /
• v.12 no.2
• /
• pp.229-241
• /
• 2013

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.28 no.9
• /
• pp.1124-1132
• /
• 2004

The inverse problem of determining unknown inlet temperature in thermally developing, hydrodynamically developed two phase laminar flow in a parallel plate duct is considered. The inlet temperature profile is determined by measuring temperature in the flow field. No prior information is needed for the functional form of the inlet temperature profile. The inverse convection problem is solved by minimizing the objective function with regularization method. The conjugate gradient method as iterative method and the Tikhonov regularization method are employed. The effects of the functional form of inlet temperature, the number of measurement points and the measurement errors are investigated. The accuracy and efficiency of these two methods are compared and discussed.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.29 no.8 s.239
• /
• pp.903-910
• /
• 2005

Inverse radiation problems are solved for estimating the boundary conditions such as temperature distribution and wall emissivity in axisymmetric absorbing, emitting and scattering medium, given the measured incident radiative heat fluxes. Various regularization methods, such as hybrid genetic algorithm, conjugate-gradient method and finite-difference Newton method, were adopted to solve the inverse problem, while discussing their features in terms of estimation accuracy and computational efficiency. Additionally, we propose a new combined approach that adopts the hybrid genetic algorithm as an initial value selector and uses the finite-difference Newton method as an optimization procedure.

• Proceedings of the KSME Conference
• /
• 2001.11b
• /
• pp.254-259
• /
• 2001

This study deals with the use of the conjugate gradient method for the simultaneous estimation of two unknown boundary heat fluxes on the slab in reheating furnace. Temperature measurements by the experiment are used in the inverse analysis. The heat flux estimations for three different cases of measurement locations in the slab are performed: non-skid, skid, and shift-skid zones. The estimated heat fluxes for three cases indicated the three regions having local peak values of heat fluxes. The estimated temperatures at measurement locations were in good agreements with the measured temperatures within 5% relative error.