• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.11
• /
• pp.5164-5171
• /
• 2011

In fuzzy modeling for nonlinear process, typically using the given data, the fuzzy rules are formed by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is identified by selection of the input variables, the number of space division and membership functions and the consequent part of the fuzzy rule is identified by polynomial functions in the form of simplified and linear inference. In general, formation of fuzzy rules for nonlinear processes using the given data have the problem that the number of fuzzy rules exponentially increases. To solve this problem complex nonlinear process can be modeled by separately forming the fuzzy rules by means of fuzzy division of each input space. Therefore, this paper utilizes individual input space to generate fuzzy rules. The premise parameters of the fuzzy rules are identified by Min-Max method using the minimum and maximum values of input data set and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. And lastly, using the data which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Journal of IKEEE
• /
• v.4 no.2 s.7
• /
• pp.192-201
• /
• 2000

This paper discusses the influence of the input conductance on system stability for the continuous type Hopfield Neural Networks.. The input conductance is connected from the neuron input to ground. The input conductance has been proved to effect on stability in input space. Transient analysis is used to test the stability in input space. Also, it has been studied how to adjust the input conductance for improving the system's performance.

• Journal of the Institute of Electronics Engineers of Korea TE
• /
• v.39 no.3
• /
• pp.97-104
• /
• 2002

In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space

• Journal of Electrical Engineering and Technology
• /
• v.9 no.6
• /
• pp.2098-2106
• /
• 2014

In the area of clustering, there are numerous approaches to construct clusters in the input space. For regression problem, when forming clusters being a part of the overall model, the relationships between the input space and the output space are essential and have to be taken into consideration. Conditional Fuzzy C-Means (c-FCM) clustering offers an opportunity to analyze the structure in the input space with the mechanism of supervision implied by the distribution of data present in the output space. However, like other clustering methods, c-FCM focuses on the distribution of the data. In this paper, we introduce a new method, which by making use of the ambiguity index focuses on the boundaries of the clusters whose determination is essential to the quality of the ensuing classification procedures. The introduced design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the fuzzy classifiers and quantify several essentials design aspects.

• International Journal of Aeronautical and Space Sciences
• /
• v.14 no.2
• /
• pp.183-192
• /
• 2013

This paper presents formation reconfiguration using impulsive control input for spacecraft formation flying. Spacecraft in a formation should change the formation size and/or geometry according to the mission requirements and space environment. To modify the formation radius and geometry with respect to the leader spacecraft, the follower spacecraft generates additional control inputs; the two impulsive control inputs are general control type of the spacecraft system. For the impulsive control input, Lambert's problem is modified to construct the transfer orbit in relative motion, given two position vectors at the initial and final time. Moreover, the numerical simulation results show the transfer trajectories to resize the formation radius in the radial/along-track plane formation and in the along-track/cross-track plane formation. In addition, the maneuver characteristics are described by comparing the differential orbital elements between the reference orbit and transfer orbit in the radial/along-track plane formation and along-track/cross-track plane formation.

• Smart Structures and Systems
• /
• v.13 no.1
• /
• pp.99-109
• /
• 2014

The goal of any Model Order Reduction (MOR) technique is to build a model of order lower than the one of the real model, so that the computational effort is reduced, and the ability to estimate the input-output mapping of the original system is preserved in an important region of the input space. Actually, since only a subset of the input space is of interest, the matching is required only in this subset of the input space. In this contribution, the consequences on the achieved accuracy of adopting different reduction technique patterns is discussed mainly with reference to a linear case study.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.22 no.5
• /
• pp.17-23
• /
• 1985

This paper describes a method for the design of a self-tuning controller of single-input/single-output systems with system noises and obsrrvation noises. The method uses state-space techniques to assign the closed-loop system poles to desired locations, but the control law is made up of process input and output measurement values, so that state estimation is unnecessary. Also the difficulties of tracking of reference inputs in state.space pole-placing control are tackled by including the reference input in the cost function proposed by Beger.

• Smart Structures and Systems
• /
• v.8 no.2
• /
• pp.157-172
• /
• 2011

A new method for both local damage(s) identification and input excitation force identification of beam structures is presented using the dynamic response sensitivity-based finite element model updating method. The state-space approach is used to calculate both the structural dynamic responses and the responses sensitivities with respect to structural physical parameters such as elemental flexural rigidity and with respect to the force parameters as well. The sensitivities of displacement and acceleration responses with respect to structural physical parameters are calculated in time domain and compared to those by using Newmark method in the forward analysis. In the inverse analysis, both the input excitation force and the local damage are identified from only several acceleration measurements. Local damages and the input excitation force are identified in a gradient-based model updating method based on dynamic response sensitivity. Both computation simulations and the laboratory work illustrate the effectiveness and robustness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.2
• /
• pp.436-444
• /
• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• The Bulletin of The Korean Astronomical Society
• /
• v.46 no.1
• /
• pp.41.2-41.2
• /
• 2021

In this study, we generate He I 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He I 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single input SDO/AIA 19.3 nm image for Model I, single input 30.4 nm image for Model II, and double input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He I 1083 nm images with high correlations. Second, the model with two input images shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean squared error (RMSE). CC and RMSE between real and AI-generated ones for the model III with 4 by 4 binnings are 0.84 and 11.80, respectively. Third, AI-generated images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He I 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of chromosphere and coronal holes.