In this paper, a parameter identification method for a discrete-time linear system using multi-layer neural network is proposed. The parameters are identified with the combination of weights and the output of neuraons of a neural network, which can be used for a linear and a nonlinear controller. An adaptive output tracking architecture is designed for the linear controller. And, the nonlinear controller. A sliding mode control law is applied to the stabilizing the nonlinear controller such that output errors can be reduced. The effectiveness of the proposed control scheme is illustrated through simulations.

In this paper, we consider a generalized mixed H_2/H_{\infty}$ output feedback problem. It is finding an internally stabilizing controller that minimizes a mixed $H_{2}$/ $H_{performance}$ measure. We show that a generalized mixed H_2/H_{\infty}$ system with two exogenous inputs and two controlled signals is transformed into auxiliary system with two exogenous inputs and one controlled signal. The two systems have equivalent performance. Therefore, a complete solution of generalized mixed H_2/H_{\infty}$ output feedback problem is achieved by existing results of mixed H_2/H_{\infty}$ control theory.

In this paper, we present a robust performance improvement method for the NLCF(normalized left coprime factor) uncertain structure using loop shaping and the structure singular value. For this, we select weighting functions for a loop shaping considering condition numer, and transform the NLCF uncertain structure into the 4-block structure. However, we can't get a good performance on account of the restriction of weighting functions. To cope with this, we motivate the use of structured singular vlaue in the robust performance improvement procedure. After all, the robust performance improvement can be obtained by a factor W$_{a}$ and a scaling factor of D-K iteration.

This paper will propose the direct adaptive learning control scheme based on adaptive control technique and intelligent control theory for a nonlinear system. Using the proposed learning control scheme, we will apply to on-line control an uncertain but for model perfect matching, it's structure condition is known. The effectiveness of the proposed control schem will be illustrated by simulations of a robot manipulator.

In this paper, we propose the discrete model reduction method of bounded real transfer functions. From the discrete bounded real lemma, we obtain the two riccati equations and define the disrete bounded real balancing using solutions of these two riccati equations. And we get the reduced order discrete model from the GSPA of full order model. Especially, when free parameter of GSPA is .+-.1, we show that the reduced order discrete model retains minimality, stability, and bounded real and BR-balancing properties. And we derive the .inf.-norm error bound between full order model and reduced order model. Finally to illustrate the validity of proposed method, we give an example.

In this paper, anew parallel processing system based on a cluster architecture which provides scalability of a parallel processing system while maintains shared memory multiprocessor characteristics is proposed. In recent days low cost, high performnce microprocessors have led to construction of large scale parallel processing systems. Such parallel processing systems provides large scalability but are mainly used for scientific applications which have large data parallelism. A shared memory multiprocessor system like TICOM is currently used as aserver for the commercial application, however, the shared memory multiprocessor system is known to have very limited scalability. The proposed architecture can support scalability and performance of the parallel processing system while it provides adaptability for the commerical application, hence it can overcome the limitation of the shared memory multiprocessor. The architecture and characteristics of the proposed system shall be described. A proprietary hierarchical crsossbar network is designed for this system, of which the protocol, routing and switching technique and the signal transfer technique are optimized for the proposed architecture. The design trade-offs for the network are described in this paper and with simulation usihng the SES/workbench, it is explored that the network fits to the proposed architecture.

The boroadband ISDN will transprot the traffics for a wide range of applications with different quality-of-service (QOS) requirements and the priorit control mechanism is an effective method to support multiple classes of services. This paper proposes a new mechanism to satisfy simultaneously the different levels of cell loss performance for the two classes of heterogeneous nonreal-time ATM traffics as well as the delay and loss requirements of real-time traffics. Its performance is analyzed using the stochastic integral approach with the cell arrivals of input streams modeled as markov modulated poisson processes.

In this paper, a enw model-base three-dimensional (3-D) object recognition mehtod using hopfield network is proposed. To minimize deformation of feature values on 3-D rotation, we select 3-D shape features and 3-D relational features which have rotational invariant characteristics. Then these feature values are normalized to have scale invariant characteristics, also. The input features are matched with model features by optimization process of hopjfield network in the form of two dimensional arrayed neurons. Experimental results on object classification and object matching with the 3-D rotated, scale changed, an dpartial oculued objects show good performance of proposed method.

In this paper, an efficient algorithm for recognizing and positioning occuluded objects in a two-dimensional plane is presented. Model objects and unknown input image are approximated by polygonal boundaries, which are compactly represented by shape functions of the polygons. The input image is partitioned into measningful segments whose end points are at the locations of possible occlusion - i.e. at concave vertices. Each segment is matched against known model objects by calculating a matching measure, which is defined as the minimum euclidean distance between the shape functions. An O(mm(n+m) algorithm for computing the measure is presentd, where n and m are the number of veritces for a model and an unknown object, respectively. Match results from aprtial segments are combined based on mutual compatibility, then are verified using distance transformation and translation vector to produce the final recognition. The proposed algorithm is invariant under translation and rotation of objects, which has been shown by experimental results.

This paper describes a new method for reducing blocking effet in block coded images using wavelet transform. In this method, all processings are one-dimensionally executed based on the fact that blocking effect occures both horizontal and vertical directions in image. Experimental results show that the proposed method yields PSNR improvement and better subjective quality.

When objects are scanned spatially by a laser-beam and mechanical mirror scanners, spatial information can be obtained, and then it is improtant to accurately obtain the parameters relating the light source and camera positions, etc.. In this paper, a calibration technique is presented for correction of measuremtn errors in a three-dimensional laser scanner system with two galvanometers. First, a model of the systematic errors is developed based on the geometry of the scanning system. Calibration parameter values are then iteratively adjusted with coarse-fine search in order to minimize errors (evaluation function) between measured and computed distances. It is shown that this correction method results in measurement precision suitable for practical use.

Adaptive resonance theory (ART2) characterized by its built-in mechanism of handling the stability-plasticity switching and by the adaptive learning without forgetting informations learned in the past, is based on an unsupervised template matching. We propose an improved tow-stage learning algorithm for aRT2: the original unsupervised learning followed by a new supervised learning. Each of the output nodes, after the unsupervised learning, is labeled according to the category informations to reinforce the template pattern associated with the target output node belonging to the same category some dominant classes from exhausting a finite number of template patterns in ART2 inefficiently. Experimental results on a set of 2545 FLIR images show that the ART2 trained by the two-stage learning algorithm yields better accuracy than the original ART2, regardless of th esize of the network and the methods of evaluating the accuracy. This improvement shows the effectiveness of the two-stage learning process.

For each reference station and user, the proposed DGPS system is implemented by a GPS module and a modem. The reference station makes plans of the receiving schedule from the satellite sets at each period and then provides the correction data for various satellite sets in a period. Users can utilize the correction data continuously and efficiently through the recursive least squares lattice filters. Furthermore, after taking DGPS techniques, we can improve the positioning accuracy using a kalman filter by decreasing anti-common errors between a reference station and a user. Experimental results show the positioning accuracy of better than 5 meters in almost real time.

In object analysis by image processing, finding lines plays a universal role. And these lines can be easily found by detecting ridges and valleys in digital gray scale images. In this paper, a new method of detecting ridges and valleys by using local min/max operations was presented. This method detects ridges and valleys of desired width by using erosion and dilation properties of local min/max operations, and requires no information of ridge or valley direction. Therefore the method is efficient and computationally simple in comparision with the conventional analytical method.

In this paper, an algorithm for on-line rightward japanese parsing is proposed. The ambiguity in on-line parsing is accumulated until the input is completely finished, since there is not a space between words in the japanese sentence. Thus the algorithm for morphological analysis, based on modified chart, is used in solving it. And the number of searching a word in dirctionary for morphological analysis is also a puzzling problem. The japanese sentence, consist of N characters, has logically its maximum number of N(N+1)/2 searches in the ordinary on-line analysis, which is nearly twice as many as normal off-line. In this paper, the matter is settled through the modification of dictionary format. In experiment, we can accomplish the rate of analysis which is nearly equal to that of off-line parsing. And it becomes clear that the longer a sentence is, the better an analysis efficiency is.

This paper proposes a genetic fuzzy controller ensemble (FCE) for improving the control performance of of fuzzy controller in the non-linear and complex problems. The design procedure of each fuzzy controller in the FCF consists of the following two stages, each of which is performed by different genetic algorithms. The first stage generates a fuzzy rule base that covers the training examples as many as possible. The second stage builds fine-tuned membership funcitons that make the control error as small as possible. These two stages are repeated independently upon the different partition patterns of input-output variables. The control performance of the proposed method is compared with that of wang and mendel's approach[1] in terms of either the percentage of successful controls reaching to the goal or the average traveling distance.

A novel resonant pulse control technique which generates high-quality sinusoidal output voltage from a resonant dc link inverter is presented for UPS applicatons. The proposed control technique limits resonant voltae overshoot without any passive or active clamp circuit, resulting in resonant pulses iwth uniform amplitude and high efficiency. The output voltage is controlled by the third order contorller iwth an inner loop of th efilter inductor current and the feedforward controller. Analysis and design of the proposed control technique are illustrated and verified on a 5kVA experimental unit.

In this paper, a novel control strategy of curretn source PWM converter and inverter system for induction motor dirves is proposed. The PWM converter controls line current to be sinusoidal and makes input power factor unity. In the inverter part, a minor voltage control loop is incorporated in the current control looop in order to suppress unstale resonance between the filter capacitor and the motor leakage inductance and to keep the stator voltage under the rated value. In additon, the modulation index control of the inverter currents ripples and inverter loss. With the proposed algorithm, both high dynamic responses and satisfactory static performance can be obtained.