• Proceedings of the KIEE Conference
• /
• 2002.07d
• /
• pp.2821-2823
• /
• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.534-541
• /
• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.

• Journal of Korea Water Resources Association
• /
• v.41 no.11
• /
• pp.1079-1094
• /
• 2008

This study is to evaluate the impact of varying spatial resolutions on the uncertainty of Soil and Water Assessment Tool (SWAT) predicted streamflow, non-point source (NPS) pollution loads transport in a small agricultural watershed (1.21 $km^2$) for three cases of model input; Case A is the combination of 2 m DEM, QuickBird land use, Case B is the combination of 10 m DEM, 1/25,000 land use, and Case C is the combination of 30 m DEM, Landsat land use, soil data is used 1/25,000 for three cases respectively. The model was calibrated for 2 years (1999-2000) using daily streamflow and monthly water quality records, and verified for another 2 years (2001-2002). The average Nash and Sutcliffe model efficiency was 0.59 for streamflow and RMSE were 2.08, 4.30 and 0.70 tons/yr for sediment, T-N and T-P respectively. The model was run for a small agricultural watershed with three cases of spatial input data. The hydrological results showed that output uncertainty was biggest by spatial resolution of land use. Streamflow increase the watershed average CN value of QucikBird land use was 0.4 and 1.8 higher than those of 1/25,000 and Landsat land use caused increase of streamflow. On the other hand, The NPS loadings from the model prediction showed that the sediment, T-N and T-P of QuickBird land use (Case A) showed 23.7 %, 43.3 % and 48.4 % higher value than 1/25,000 land use (Case B) and 50.6 %, 50.8 % and 56.9 % higher value than Landsat land use (Case C) respectively.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.10
• /
• pp.991-997
• /
• 2008

A new RMRAC scheme far the PMSM current regulation is proposed in a synchronous frame, which is completely free from the parameter's uncertainty. A current regulator of PMSM is the inner most loop of electromechanical driving systems and plays a foundation role in the control hierarchy. When the PMSM runs in high speed, the cross-coupling terms must be compensated precisely for large system BW. In the proposed RMRAC, the input signal is composed of a calculated voltage defined by MRAC law and an output of the disturbance compensator. The gains of feed forward and feedback controller are estimated by the proposed modified gradient method, where the system disturbances are assumed as filtered current regulation errors. After the compensation of the system disturbance from error information, the corresponding voltage is fed forward to control input to compensate for real disturbances. The proposed method robustly compensates the system disturbance and cross-coupling terms. It also shows a good realtime performance due to the simplicity of control structure. Through real experiments, the efficiency of the proposed method is verified.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.5
• /
• pp.705-711
• /
• 2013

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

• Journal of Computing Science and Engineering
• /
• v.2 no.2
• /
• pp.200-219
• /
• 2008

The effective management of uncertainty is one of the most fundamental problems in medical decision making. According to the literatures review, most medical decision models rely on point estimates for input parameters. However, it is natural that they should be interested in the relationship between changes in those values and subsequent changes in model output. Therefore, the purpose of this study is to identify the ranges of numerical values for which each option will be most efficient with respect to the input parameters. The Nonhomogeneous Poisson Process(NHPP) was used for describing the behavior of aging chronic diseases. Three kind of failure models (linear, exponential, and power law) were considered, and each of these failure models was studied under the assumptions of unknown scale factor and known aging rate, known scale factor and unknown aging rate, and unknown scale factor and unknown aging rate, respectively. In addition, this study illustrated developed method with an analysis of data from a trial of immunotherapy in the treatment of chronic Granulomatous disease. Finally, the proposed design of Bayesian value of information analysis facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert's opinions and the sampling information which will furnish decision makers with valuable support for quality medical decision making.

• Journal of Power Electronics
• /
• v.11 no.5
• /
• pp.719-725
• /
• 2011

This paper describes a novel Direct Torque Control (DTC) method for adjustable speed Doubly-Fed Induction Machine (DFIM) drives which is supplied by a two-level Space Vector Modulation (SVM) voltage source inverter (DTC-SVM) in the rotor circuit. The inverter reference voltage vector is obtained by using input-output feedback linearization control and a DFIM model in the stator a-b axes reference frame with stator currents and rotor fluxes as state variables. Moreover, to make this nonlinear controller stable and robust to most varying electrical parameter uncertainties, a two layer recurrent Artificial Neural Network (ANN) is used to estimate a certain function which shows the machine lumped uncertainty. The overall system stability is proved by the Lyapunov theorem. It is shown that the torque and flux tracking errors as well as the updated weights of the ANN are uniformly ultimately bounded. Finally, effectiveness of the proposed control approach is shown by computer simulation results.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.13 no.3
• /
• pp.59-67
• /
• 1993

The variations of the natural frequencies and the peak response acceleration at the top of prestressed concrete reactor building due to random variability and/or model uncertainty of structural parameters are studied. The results may be used as essential input parameters in seismic probabilistic risk assessment or seismic margin assessment of the reactor building. The sensitivity test of each structural parameter is first performed to determine the most influential parameter upon the natural frequency of structure model. Then Monte Carlo simulation technique is applied to evaluate the effect of parameter variation on the natural frequencies and the peak response acceleration. The acceleration time history is obtained by direct integration scheme. As the study results, it is found that the fundamental natural frequency and the peak response acceleration at the top of the building are most strongly affected by Young's modulus among the structural parameters, in which the value of mean plus one standard deviation obtained by probabilistic approach deviates up to about (+)12% from the result of deterministic method. Considering the uncertainty of flexural rigidity, the structural responses vary in range of (-)4%~(+)14%.

• The Korean Journal of Franchise Management
• /
• v.13 no.3
• /
• pp.45-55
• /
• 2022

Purpose: Consumers' experience of functional attributes is remembered, and the experience lowers the cost of consumers' input from their point of view and reduces uncertainty. It also plays an important role in consumers' positive emotions and responses. Accordingly, if information search costs are reduced in terms of the costs perceived by consumers about restaurants, a strategy differentiated from other companies can be established. Therefore, this study investigated the effect of functional congruence of restaurant stores on information search cost reduction, positive/negative emotions, and loyalty. Research Design, Data, and Methodology: This study investigated functional congruence, information search cost reduction, and positive/negative emotions. The structural relationship between loyalty was analyzed. To verify this, a research hypothesis was established based on previous studies and a research model was constructed. The questionnaire items were modified and used according to the current study, based on previous studies. The data were collected using the questionnaire method from 187 people who had dining out experience. Frequency analysis was performed to confirm demographic characteristics. Reliability, convergent validity, and discriminant validity of the collected data were verified. The research model was analyzed with a structural equation modeling (SmartPLS 4). Results: The findings show that functional congruence had significant positive effects on information search cost reduction and positive emotion, but no significant effect on negative emotion. Information search cost reduction had significant positive effects on positive emotion/negative emotion but did not significantly affect loyalty. Lastly, both positive and negative emotions had significant positive effects on loyalty. Conclusion: Based on transaction cost theory, this study found how functional congruence and information search cost reduction influence consumers' emotions. The functional attributes of restaurants were perceived by customers as information, thus uncertainty was decreased. Finally, appropriate management strategies and implications of functional congruence and information search cost in the restaurant were suggested.

• Steel and Composite Structures
• /
• v.51 no.1
• /
• pp.25-41
• /
• 2024

The measurement of pile bearing capacity is crucial for the design of pile foundations, where in-situ tests could be costly and time needed. The primary objective of this research was to investigate the potential use of fuzzy-based techniques to anticipate the maximum weight that concrete driven piles might bear. Despite the existence of several suggested designs, there is a scarcity of specialized studies on the exploration of adaptive neuro-fuzzy inference systems (ANFIS) for the estimation of pile bearing capacity. This paper presents the introduction and validation of a novel technique that integrates the fire hawk optimizer (FHO) and equilibrium optimizer (EO) with the ANFIS, referred to as ANFIS_FHO and ANFIS_EO, respectively. A comprehensive compilation of 472 static load test results for driven piles was located within the database. The recommended framework was built, validated, and tested using the training set (70%), validation set (15%), and testing set (15%) of the dataset, accordingly. Moreover, the sensitivity analysis is performed in order to determine the impact of each input on the output. The results show that ANFIS_FHO and ANFIS_EO both have amazing potential for precisely calculating pile bearing capacity. The R² values obtained for ANFIS_FHO were 0.9817, 0.9753, and 0.9823 for the training, validating, and testing phases. The findings of the examination of uncertainty showed that the ANFIS_FHO system had less uncertainty than the ANFIS_EO model. The research found that the ANFIS_FHO model provides a more satisfactory estimation of the bearing capacity of concrete driven piles when considering various performance evaluations and comparing it with existing literature.