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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Korean Institute of Intelligent Systems
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Korean Institute of Intelligent Systems
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Volume & Issues
Volume 6, Issue 4 - Dec 1996
Volume 6, Issue 3 - Sep 1996
Volume 6, Issue 2 - Jun 1996
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Design of a Sliding Mode controller with Self-tuning Boundary Layer
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 3~12
Sliding mode controller(SMC) is a simple but powerful nonlinear controller, because it guarantees the stability and the robustness. However, it leads to the high frequency chattering of the control input. Although the phenomenon can be avoided by introducing a thin boundary layer to the sliding surface, the method results in a steady state: error proportional to the boundary layer thickness. In this paper, we proposed a new sliding mode controller with self-tuning the thickness of a boundary layer. It uses a fuzzy rule base for tuning the thickness of a boundary layer. That is, the thickness is increased to some degree to reject a discontinuous control input at the initial state and then it is decreased as the states approaches to the steady states for improving the tracking performance. In order to assure the control performance, we perf'ormed the computer simulation using an inverted pendulum system as a controlled plant.
Evolutionary Learning of Sigma-Pi Neural Trees and Its Application to classification and Prediction
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 13~21
The necessity and usefulness of higher-order neural networks have been well-known since early days of neurocomputing. However the explosive number of terms has hampered the design and training of such networks. In this paper we present an evolutionary learning method for efficiently constructing problem-specific higher-order neural models. The crux of the method is the neural tree representation employing both sigma and pi units, in combination with the use of an MDL-based fitness function for learning minimal models. We provide experimental results in classification and prediction problems which demonstrate the effectiveness of the method. I. Introduction topology employs one hidden layer with full connectivity between neighboring layers. This structure has One of the most popular neural network models been very successful for many applications. However, used for supervised learning applications has been the they have some weaknesses. For instance, the fully mutilayer feedforward network. A commonly adopted connected structure is not necessarily a good topology unless the task contains a good predictor for the full *d*dWs %BH%W* input space.
A Not on a Construction of t-norm
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 22~25
In this paper, we consider a generalized problem of Fuller's [Fuzzy Sets and System 45 pp. 299-303, 1992] open question and prove it.
Nonlinear Approximations Using RBF Neural Networks
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 26~35
In this paper, some fundamental problems concerning RBF(radial-basis-function) networks and approximation of functions are addressed. First, a comprehensive introduction to RBF networks is given with typical RBF networks classified into three classes. Next, sharp conditions are given under which continuous functions of a finite number of real variables can be approximated arbitrarily well by a certain class of RBF networks. Finally, a related result is given concerning the representation of functions in the form of distributed RBF networks.
A Study on the Auto-Tuning of a PID Controller using Artificial Neural Network
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 36~42
In this paper, we proposed a PID controller, which could control unknown plants using Artificial Neural Network(ANN) for auto-tuning of the PID parameters. In the proposed algorithm, the parameters of the controller were adjusted to reduce the error of the controlled plant. In this process, the sensitivity between input and output of the unknown plant was needed. So, in order to obtain this sensitivity, the ANN's learnig ability was used. Computer simualtions were performed for the regulation problems, and the results were compared with those of Ziegler-Nichols PID controller. As a result, it was shown that the proposed algorithm outperformed Ziegler-Nichols controller in rise time, overshoot, undershoot, and setting time.
Fuzzy Rule Optimization Using Genetic Algorithms with Adaptive Probability
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 43~51
Fuzzy rules in fuzzy logic control play a major role in deciding the control dynamics of a fuzzy logic controller. Thus, control performance is mainly determined by the quality of fuzzy rules. This paper introduces an optimization method for fuzzy rules using GAS with adaptive probabilies of crossover and mutation. Also we design two fitness measures to satisfy control objectives by partitioning the response of a plant into two parts. An initial population is generated by an automatic fuzzy rule generation method instead of random selection for fast a.pproaching to the final solution. We employed a nonlinear plant to simulate our method. It is shown through simulation that our method is reasonable and can be useful for optimizing fuzzy rules. I. Introduction Fuzzy logic control (FLC) methods have been widely employed to control highly nonlinear plants from the late 1980s 11, 2, 31. In fuzzy logic control (FLC), fuzg rules play a major role for deciding the 'Schocbl of Information and Computer Engineering, Hansung control dynamics of a fuzzy logic controller [,I. Thus, Univ. @-%rHqZ %!24+!q': control performance of FLC is mainly determined by
Off-line Selection of Learning Rate for Back-Propagation Neural Ntwork using Evolutionary Adaptation
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 52~56
In trainir~ga back-propagation neural network, the learning speed of the network is greatly affected by its learning rate. Most of off-line fashioned learning-rate selection methods, however, are empirical except for some deterministic methods. It is very tedious and difficult to find a good learning rate using the empirical methods. The deterministic methods cannot guarantee the quality of the quality of the learning rate. This paper proposes a new learning-rate selection method. Our off-line fashioned method selects a good learning rate through stochastically searching process using evolutionary programming. The simulation results show that the learning speed achieved by our method is superior to that of deterministic and empirical methods.
FMFNN Modeling of the Tire Characteristics for Ground Vehicle Control
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 57~71
Speech Secure Communication Control System Using Chaos Generation Circuit
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 72~80
Traffic Rout Choice by means of Fuzzy Identification
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 81~89
A design method of fuzzy modeling is presented for the model identification of route choice of traffic problems.The proposed fuzzy modeling implements system structure and parameter identification in the eficient form of""IF..., THEN-.."", using the theories of optimization theory, linguistic fuzzy implication rules. Three kinds ofmethod for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 21,and proposed modified-linear inference (type 3). The fuzzy inference method are utilized to develop the routechoice model in terms of accurate estimation and precise description of human travel behavior. In order to identifypremise structure and parameter of fuzzy implication rules, improved complex method is used and the least squaremethod is utilized for the identification of optimum consequence parameters. Data for route choice of trafficproblems are used to evaluate the performance of the proposed fuzzy modeling. The results show that the proposedmethod can produce the fuzzy model with higher accuracy than previous other studies -BL(binary logic) model,B(production system) model, FL(fuzzy logic) model, NN(neura1 network) model, and FNNs (fuzzy-neuralnetworks) model -.fuzzy-neural networks) model -.
Stern Profile Design using Fuzzy Modeling
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 90~96
This paper presents a method that determines the stern profile dimensions for full, slwo-speed ship using fuzzy modeling, which is applied the genetic algorithm and the Hooke & Jeeves method. The infreed stern profile dimensions have compared with real ships.
A Study on an Adaptive Model Predictive Control for Nonlinear Processes using Fuzzy Model
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 97~105
In this paper, an adaptive model predictive controller for nodinear processes using fuzzy model is proposed. Adaptive structure is implemented by recursive fuzzy modeling. The model and control law can be obtained the same as GPC, because the consequent parts of the fuzzy model comprise linear equations of input and output variables. The proposed Adaptive fuzzy model predictive controller (AFMPC) controls nonlinear process well due to the intrinsic nonlinearity of the fuzzy model. When AFMPC's output is variation in the process control input, it maintains zero steady-state offset for a constant reference input and has superior performance. The properties and performance of the proposed control scheme were examined with nonlinear plant by simulation.
Fuzzy Pairwise $-continuous Mapping
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 106~110
In this paper, we define a (
set and a fuzzy pairwise fuzzy $-continuous mapping on fuzzy bitopological spaces ans study some of their properites.
Time-Optimal Control for Cooperative Multi-Robot Manipulators Based on Fuzzy Optimal Load Distributioin
Journal of Korean Institute of Intelligent Systems, volume 6, issue 2, 1996, Pages 111~119
In this paper, we propose time-optimal trajectory planning algorithms for cooperative multi-robot manipulators system considering optimal load distribution. Internal forces essentially effect on time optimal trajectory planning and if they are comitted, the time optimal scheme is not no longer true. Therefore, we try to find the internal force factors of cooperative robot manipulators system in a time-optimal aspect. In this approach, a specific generalized inverse is used and is fuzzified for the purpose. In this optimal method, the fuzzy logic concept is used and selected for diminishing computation time, for finding the load distribution factors.