• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.408-411
• /
• 1993

In this paper, we propose the Fuzzy Neural Controller with a Self-Organizing Map based on the fuzzy relation neuron. The fuzzy ndes expressing the input-output relation of the system are obtained by using the fuzzy relation neuron and updated automatically by means of the generalized delta rule. Also, the proposed method has a capability to express the knowledge acquired from the input-output data in form of fuzzy inferences rules. The learning algorithm of this fuzzy relation neuron is described. The effectiveness of the proposed fuzzy neural controller is illustrated by applying it to a number of test data sets.

• Proceedings of the KWS Conference
• /
• 2002.05a
• /
• pp.68-70
• /
• 2002

A CCD camera with a laser strip was applied to realize the automation of welding process in GMAW. The Hough transformation was used to extract the laser stripe and to obtain specific weld points. In this study, a neural network based on the generalized delta rule algorithm was adapted for the process control of GMA, such as welding speed, arc voltage and wire feeding speed.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.12 no.11
• /
• pp.1038-1047
• /
• 2000

The objective of this study is to develop an optimized Artificial Neural Network(ANN) model to predict the descending time of room air temperature. For this, program for predicting room air temperature and ANN program using generalized delta rule were collected through simulation for predicting room air temperature. ANN was trained and the ANN model having the optimized values-learning rate, moment, bias, number of hidden layer, and number of neuron of hidden layer was presented.

• Journal of Sensor Science and Technology
• /
• v.10 no.6
• /
• pp.328-337
• /
• 2001

The stochiometric mix of evaporating materials for the $CdGa_2Se_4$ single crystal thin films was prepared from horizontal furnace. To obtain the single crystal thin films, $CdGa_2Se_4$ mixed crystal was deposited on thoroughly etched semi-insulating GaAs(100) substrate by the Hot Wall Epitaxy (HWE) system. The source and substrate temperature were $630^{\circ}C$ and $420^{\circ}C$, respectively. The crystalline structure of single crystal thin films was investigated by the photoluminescence and double crystal X-ray diffraction (DCXD). The carrier density and mobility of $CdGa_2Se_4$ single crystal thin films measured from Hall effect by van der Pauw method are $8.27{\times}10^{17}cm^{-3}$, $345\;cm^2/V{\cdot}s$ at 293 K, respectively. From the photocurrent spectrum by illumination of perpendicular light on the c-axis of the $CuInSe_2$ single crystal thin film, we have found that the values of spin orbit splitting ${\Delta}So$ and the crystal field splitting ${\Delta}Cr$ were 106.5 meV and 418.9 meV at 10 K, respectively. From the photoluminescence measurement on $CdGa_2Se_4$ single crystal thin film, we observed free excition ($E_x$) existing only high quality crystal and neutral bound exiciton ($D^{\circ}$, X) having very strong peak intensity. Then, the full-width-at -half-maximum(FWHM) and binding energy of neutral donor bound excition were 8 meV and 13.7 meV, respectivity. By Haynes rule, an activation energy of impurity was 137 meV.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.9
• /
• pp.1905-1912
• /
• 2009

Clustering results based on the FCM algorithm sometimes produces undesirable clustering result through data distribution in the clustered space because data is classified by comparison with membership degree which is calculated by the Euclidean distance between input vectors and clusters. Symmetrical measurement of clusters and fuzzy theory are applied to the classification to tackle this problem. The enhanced FCM algorithm has a low impact with the variation of changing distance about each cluster, middle of cluster and cluster formation. Improved hybrid network of applying FCM algorithm is proposed to classify patterns effectively. The proposed enhanced FCM algorithm is applied to the learning structure between input and middle layers, and normalized delta learning rule is applied in learning stage between middle and output layers in the hybrid network. The proposed algorithms compared with FCM-based RBF network using Max_Min neural network, FMC-based RBF network and HCM-based RBF network to evaluate learning and recognition performances in the two-dimensional coordinated data.

• IE interfaces
• /
• v.22 no.3
• /
• pp.205-213
• /
• 2009

Warfare information system is a core factor of future combat operation under NCW(Network centric Warfare) environment. In this paper, we proposed a new MOE(Measure of Effectiveness) that can assess the combat power synergistic effect of warfare information system in the theater-level joint fire operation. This new MOE uses the rule of Newton's second law($F=(m{\Delta}{\upsilon})/{\Delta}t{\Rightarrow}(M{\upsilon}I)/T$). Four factors considered in combat power evaluation are network power(M), movement velocity(v), information superiority(I), command and control time(T). We applied this new MOE to the JFOS-K(Joint Fire Operating System-Korea) to assess its combat power effect and compared with other's effects obtained from different MOE.

• Journal of the Institute of Convergence Signal Processing
• /
• v.11 no.1
• /
• pp.88-91
• /
• 2010

In this paper, We are composed the position recognition and speed control using the moving robot in the shield Room with a RF Module and Ultrasonic Sensors. Double look up tables are selected a reference value/duty ratio. The moving robot with the dual fuzzy rules which can decrease a Conversion time than basic fuzzy control rules at start point and curve region. Also, a changing times of double look up table are rise at specific points b1,c1,d1 in the e-${\Delta}e$ phase plane and the one of the look up table is used which for increase rising time at transition area, the other used for rapidly conversion to the reference value. We verified that a dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

• Korean Journal of Materials Research
• /
• v.11 no.5
• /
• pp.419-426
• /
• 2001

The stochiometric $AgGaSe_2$ polycrystalline mixture of evaporating materials for the $AgGaSe_2$ single crystal thin film was prepared from horizontal furnace. To obtain the single crystal thin films, $AgGaSe_2$ mixed crystal and semi-insulating GaAs(100) wafer were used as source material and substrate for the Hot Wall Epitaxy (HWE) system, respectively. The source and substrate temperature were fixed at$ 630^{\circ}C$ and $420^{\circ}C$, respectively. The thickness of grown single crystal thin films is 2.1$\mu\textrm{m}$. The single crystal thin films were investigated by photoluminescence and double crystal X-ray diffraction(DCXD) measurement. The carrier density and mobility of AgGaSe$_2$ single crystal thin films measured from Hall effect by van der Pauw method are $4.89\Times10^{17}$ cm$^{-3}$ , 129cm2/V.s at 293K, respectively. From the Photocurrent spectrum by illumination of perpendicular light on the c-axis of the AgGaSe$_2$ single crystal thin film, we have found that the values of spin orbit splitting $$\Delta$S_{o}$ and the crystal field splitting $\Delta$C$_{r}$, were 0.1762eV and 0.2474eV at 10K, respectively. From the photoluminescence measurement of AgGaSe$_2$ single crystal thin film, we observed free excision (EX) observable only in high quality crystal and neutral bound exciton ($D^{o}$ , X) having very strong peak intensity. And, the full width at half maximum and binding energy of neutral donor bound excition were 8mev and 14.1meV, respectively. By Haynes rule, an activation energy of impurity was 141 meV.ion energy of impurity was 141 meV.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.36-39
• /
• 1996

This paper proposes identification and control algorithm of nonlinear systems and the proposed fuzzy-neural network has following characteristics. The network is roughly divided into premise and consequence. The consequence function is nonlinear function which consists of three parameters and the membership function in the premise contains of two parameters. The parameters in premise and consequence are learned by the extended back-propagation algorithm which has a modified form of the generalized delta rule. Simulation results on the identification show that this method is more effective than that of Narendra [3]. The indirect fuzzy-neural control is made of the fuzzy-neural identification and controller. Result on the indirect fuzzy-neural control shows that the proposed fuzzy-neural network can be efficiently applied to nonlinear systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.399-403
• /
• 1998

In this paper, we proposed a new fuzzy supervised learning algorithm. We construct, and train, a new type fuzzy neural net to model the linear activation function. Properties of our fuzzy neural net include : (1) a proposed linear activation function ; and (2) a modified delta rule for learning algorithm. We applied this proposed learning algorithm to exclusive OR,3 bit parity using benchmark in neural network and pattern recognition problems, a kind of image recognition.