• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.279-286
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• 1996

In this paper, variable structure control(VSC) based on reaching law method with fuzzy inference for nonlinear systems is proposed. The reaching law means the reaching condition which forces an initial state of system to reach switching surface in finite time, and specifies the dynamics of a desired switching function. Since the conventional reaching law has fixed coefficients, the chattering can be existed largely in sliding mode. In the design of a proposed fuzzy reaching law, we fuzzify RP(representative point)'s orthogonal distance to switching surface and RP's distance the origin of the 2-dimensional space whose coordinates are the error and the error rate. The coefficients of the reaching law are varied appropriately by the fuzzy inference. Hence the state of system in reaching mode reaches fastly switching surface by the large values of reaching coefficients and the chattering is reduced in sliding mode by the small values of those. And the effectiveness of the proposed fuzzy reaching law method is showen by the simulation results of the control of a two link robot manipulator.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.1
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• pp.32-44
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• 1997

A moving sliding surface(MSS) was proposed earlier for the second-order variable structure control system(VSCS). The MSS was disigned to pass arbitrary initial conditions, and subsequently moved towards a predetermined sliding surface by rotating and/or shifting. This methodology led to fast and robust control responses of the second-order VSCS, especially in a reaching phase. However, the moving algorithm of the MSS was too complicated to be employed to the high-order VSCS. To resolve this problem, a new moving algorithm based on the fuzzy theory is proposed in this paper. For the generalization of the MSS, the conditions for rotating or shifting are firstly investigated. Then the fuzzy algorithm is formulated by adopting the values of the surface function and the total discontinuity gain as input variables, and the variation of the surface function as output variable. The position control problem of an electrohydraulic servomechanism is adopted in order to demonstrate the efficiency and the feasibility of the proposed MSS associated with fuzzy algorithm.

• Advances in concrete construction
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• v.11 no.1
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• pp.1-9
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• 2021

This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.41-51
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• 1996

This paper presents a fuzzy data fusion scheme which can analyze the sensor condition, the strength and location of a force applied to a test material. These can be realized by the modelling and fusioning of sensor signals and sensor properties. The technique uses, as the inference variables, relative magnitude of data (RMD), absolute magnitude of data (AMD) initial state (IS), synchronized relational function (SRF) and asynchronized relational function (ARF). To show the usefulness of this scheme, an experiment on the cantilever bar and six strain gages is carried out. The location of the force is inferred from SRF and ARF and the strength from RMD and AMD. In particular, the strength is compared with the measurement data of the force sensor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1630-1636
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• 2022

The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.

• Proceedings of the KOR-KST Conference
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• 1995.12a
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• pp.77-107
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• 1995

This paper documents the development of a fuzzy logic based incident detection model for urban diamond interchanges. Research in incident detection for intersections and arterials is at a very initial stage. Existing algorithms are still far from being robust in dealing with the difficulties related with data availability and the multi-dimensional nature of the incident detection problem. The purpose of this study is to develop a new real-time incident detection model for urban diamond interchanges. The development of the algorithm is based on fuzzy logic. The incident detection model developed through this research is capable of detecting lane¬blocking incidents when their effects are manifested by certain patterns of deterioration in traffic conditions and, thereby, adjustments in signal control strategies are required. The model overcomes the boundary condition problem inherent in conventional threshold-based concepts. The model captures system-wide incident effects utilizing multiple measures for more accurate and reliable detection, and serves as a component module of a real-time traffic adaptive diamond interchange control system. The model is designed to be readily scalable and expandable for larger systems of arterial streets. The prototype incident detection model was applied to an actual diamond interchange to investigate its performance. A simulation study was performed to evaluate the model's performance in terms of detection rate, false alarm rate, and mean time to detect. The model's performance was encouraging, and the fuzzy logic based approach to incident detection is promising.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.2161-2166
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• 2009

It is important for 0-6 hour nowcasting to provide for a high-quality initial condition in a meso-scale atmospheric model by a data assimilation of several observation data. The polarimetric radar data is expected to be assimilated into the forecast model, because the radar has a possibility of measurements of the types, the shapes, and the size distributions of hydrometeors. In this paper, an impact on rainfall prediction of the data assimilation of hydrometeor types (i.e. raindrop, graupel, snowflake, etc.) is evaluated. The observed information of hydrometeor types is estimated using the fuzzy logic algorism. As an implementation, the cloud-resolving nonhydrostatic atmospheric model, CReSS, which has detail microphysical processes, is employed as a forecast model. The local ensemble transform Kalman filter, LETKF, is used as a data assimilation method, which uses an ensemble of short-term forecasts to estimate the flowdependent background error covariance required in data assimilation. A heavy rainfall event occurred in Okinawa in 2008 is chosen as an application. As a result, the rainfall prediction accuracy in the assimilation case of both hydrometeor types and the Doppler velocity and the radar echo is improved by a comparison of the no assimilation case. The effects on rainfall prediction of the assimilation of hydrometeor types appear in longer prediction lead time compared with the effects of the assimilation of radar echo only.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1647-1652
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.171-174
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• 2009

Generally the neural network and the fuzzy compensative algorithm are applied to forecast the time series for power demand with a characteristic of non-linear dynamic system, but it has a few prediction errors relatively. It also makes long term forecast difficult for sensitivity on the initial condition. On this paper, we evaluate the chaotic characteristic of electrical power demand with analysis methods of qualitative and quantitative and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction, time series forecast for multi dimension using Lyapunov exponent quantitatively. We compare simulated results with the previous method and verify that the purpose one being more practice and effective than it.