• The KSFM Journal of Fluid Machinery
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• v.2 no.1 s.2
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• pp.29-34
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• 1999

To develop the efficient numerical optimization method for the design of an airfoil, an evaluation of various methods coupled with two-dimensional Naviev-Stokes analysis is presented. Simplex method and Hook-Jeeves method we used as direct search methods, and steepest descent method, conjugate gradient method and DFP method are used as indirect search methods and are tested to determine the search direction. To determine the moving distance, the golden section method and cubic interpolation method are tested. The finite volume method is used to discretize two-dimensional Navier-Stokes equations, and SIMPLEC algorithm is used for a velocity-pressure correction method. For the optimal design of two-dimensional airfoil, maximum thickness, maximum ordinate of camber line and chordwise position of maximum ordinate are chosen as design variables, and the ratio of drag coefficient to lift coefficient is selected as an objective function. From the results, it is found that conjugate gradient method and cubic interpolation method are the most efficient for the determination of search direction and the moving distance, respectively.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1377-1379
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• 1996

In this paper, each parameter was identified by the gradient descent method to overcome difficulty deciding fuzzy rules of FLC for the unknown process and the type of membership Junctions. Usually PID or optimal control theories have been mostly usee in control field so far. However, optimal control requires much time for calculation because of adaptation for disturbance and nonlinearity. And intricate technique such as MRAS which can be realized only by an expert are limited to be used in the systems requiring rapid and precise response because of comparatively longer calculating time and complicateness. Gradient descent method is a method to find Z minimizing a function about a certain vector Z. And required output of FLC is gained using gradient approaching method in order to adapt control rule parameters of FLC. Simulation proved validation of this algorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.448-448
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• 2018

In this study, the regression models (Load ESTimator and eight-parameter model) were evaluated to estimate instantaneous pollutant loads under various criteria and optimization methods. As shown in the results, LOADEST commonly used in interpolating pollutant loads could not necessarily provide the best results with the automatic selected regression model. It is inferred that the various regression models in LOADEST need to be considered to find the best solution based on the characteristics of watersheds applied. The recently developed eight-parameter model integrated with Genetic Algorithm (GA) and Gradient Descent Method (GDM) were also compared with LOADEST indicating that the eight-parameter model performed better than LOADEST, but it showed different behaviors in calibration and validation. The eight-parameter model with GDM could reproduce the nitrogen loads properly outside of calibration period (validation). Furthermore, the accuracy and precision of model estimations were evaluated using various criteria (e.g., $R^2$ and gradient and constant of linear regression line). The results showed higher precisions with the $R^2$ values closed to 1.0 in LOADEST and better accuracy with the constants (in linear regression line) closed to 0.0 in the eight-parameter model with GDM. In hence, based on these finding we recommend that users need to evaluate the regression models under various criteria and calibration methods to provide the more accurate and precise results for pollutant load estimations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1679-1687
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• 2016

Mobile robots are effectively used in industrial fields that require flexible manufacturing systems. Mobile robots have to move with mechanical loads such as product parts along the specified paths, and are usually equipped with kinematic controllers. When the loads and nonlinear frictions are too high, satisfactory control performances can not be expected with the kinematic controllers, so some dynamic controllers have been developed. Conventional dynamic controllers require the exact weights and locations of the loads; however, the loads are frequently changed and unknown so that the control performances of the conventional controllers are limited. This paper proposes an adaptive PD control method using gradient descent learning to have sufficient dynamic control performance for unknown loads. Simulation studies have been conducted for various load conditions to verify that the adaptive PD control method have much broader convergence region than the convention method.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1125-1132
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• 2017

In a situation where the severity and frequency of drought events getting stronger and higher, many studies related to drought forecast have been conducted to improve the drought forecast accuracy. However it is difficult to predict drought events using a single model because of nonlinear and complicated characteristics of temporal behavior of drought events. In this study, in order to overcome the shortcomings of the single model approach, we first build various single models capable to explain the relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and other independent variables such as world climate indices. Then, we developed a combined models using Stochastic Gradient Descent method among Ensemble Learnings.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.3
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• pp.505-513
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• 2009

Z. Cao had proposed NFRM(new fuzzy reasoning method) which infers in detail using relation matrix. In spite of the small inference rules, it shows good performance than mamdani's fuzzy inference method. But the most of fuzzy systems are difficult to make fuzzy inference rules in the case of MIMO system. The past days, We had proposed the MIMO fuzzy inference which had extended a Z. Cao's fuzzy inference to handle MIMO system. But many times and effort needed to determine the relation matrix elements of MIMO fuzzy inference by heuristic and trial and error method in order to improve inference performances. In this paper, we propose a MIMO fuzzy inference method with the learning ability witch is used a gradient descent method in order to improve the performances. Through the computer simulation studies for the inverse kinematics problem of 2-axis robot, we show that proposed inference method using a gradient descent method has good performances.

• The Journal of Korean Institute of Information Technology
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• v.18 no.5
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• pp.55-64
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• 2020

Estimating the state of charge (SOC) of a battery is essential for increasing the stability and reliability of a photovoltaic system. In this study, battery SOC estimation methods were proposed using artificial neural networks (ANNs) with gradient descent (GD), Levenberg-Marquardt (LM), and scaled conjugate gradient (SCG), and an adaptive neuro-fuzzy inference system (ANFIS). The charge start voltage and the integrated charge current were used as input data and the SOC was used as output data. Four models (ANN-GD, ANN-LM, ANN-SCG, and ANFIS) were implemented for battery SOC estimation and compared using MATLAB. The experimental results revealed that battery SOC estimation using the ANFIS model had both the highest accuracy and highest convergence speed.

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

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.87-92
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• 2021

Bias, variance, error and learning are important factors for performance in modeling a big data based recommendation system. The recommendation model in this system must reduce complexity while maintaining the explanatory diagram. In addition, the sparsity of the dataset and the prediction of the system are more likely to be inversely proportional to each other. Therefore, a product recommendation model has been proposed through learning the similarity between products by using a factorization method of the sparsity of the dataset. In this paper, the generalization ability of the model is improved by applying the max-norm regularization as an optimization method for the loss function of this model. The solution is to apply a stochastic projection gradient descent method that projects a gradient. The sparser data became, it was confirmed that the propsed regularization method was relatively effective compared to the existing method through lots of experiment.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1097-1112
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• 1989

An iterative learning control scheme is incorporated to the computed torque method as a means to enhance the accuracy and the flexibility. A learning rule is constructed by utilizing a gradient descent algorithm and data compressing techniques are illustrated. Computer simulation results show a good performance of the scheme under a relatively high speed and a heavy payload condition.