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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 8, Issue 6 - Nov 1998
Volume 8, Issue 5 - Oct 1998
Volume 8, Issue 4 - Aug 1998
Volume 8, Issue 3 - Jun 1998
Volume 8, Issue 2 - Apr 1998
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Performance Improvement of Genetic Programming Based on Reinforcement Learning
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 1~8
This paper proposes a reinforcement genetic programming based on the reinforcement learning method for the performance improvement of genetic programming. Genetic programming which has tree structure program has much flexibility of problem expression because it has no limitation in the size of chromosome compared to the other evolutionary algorithms. But worse results on the point of convergence associated with mutation and crossover operations are often due to this characteristic. Therefore the sizes of population and maximum generation are typically larger than those of the other evolutionary algorithms. This paper proposes a new method that executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. The validity of the proposed method is evaluated by appling it to the artificial ant problem.
Adaptive Learning Control fo rUnknown Monlinear Systems by Combining Neuro Control and Iterative Learning Control
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 9~15
This paper presents an adaptive learning control method for unknown nonlinear systems by combining neuro control and iterative learning control techniques. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary adaptive and learning manipulation and a short term storage of a specific temporary action. The learning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memory-based feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored infonnations. This scheme is applied to a two link robot manipulator through simulations.
Features Extraction of Remote Sensed Multispectral Image Data Using Rough Sets Theory
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 16~25
In this paper, we propose features extraction method using Rough sets theory for efficient data classifications in hyperspectral environment. First, analyze the properties of multispectral image data, then select the most efficient bands using discemibility of Rough sets theory based on analysis results. The proposed method is applied Landsat TM image data, from this, we verify the equivalence of traditional bands selection method by band features and bands selection method using Rough sets theory that pmposed in this paper. Finally, we present theoretical basis to features extraction in hyperspectral environment.
Robust Fuzzy controller Design for Uncertain Nonlinear systems
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 26~32
A study on Modified Method of Orthogonal Neural Network for Nonlinear system approximation
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 33~40
This paper presents an Modified Orthogonal Neural Network(MONN), new modified model of Orthogonal Neural Network(0NN) based on orthogonal functions, and applies it to nonlinear system approximator. ONN proposed by Yang and Tseng, doesn't have the problems of traditional multilayer feedforward neural networks such as the determination of initial weights and the numbers of layers and processing elements. And tranining of ONN converges rapidly. But ONN cannot adapt its orthogonal functions to a given system. The accuracy of ONN, in terms of the minimal possible deviation between system and approximator, is essentially dependent on the choice of basic orthogonal functions. In order to improve ability and effectiveness of approximate nonlinear systems, MONN has an input transformation layer to adapt its basic orthogonal functions to a given nonlinear system. The results show that MONN has the excellent performance of approximate nonlinear systems and the input transfnrmation makes the ability of MONN better than one of ONN.
An Agent-based Fuzzy Inference System for Hull Form Design
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 41~49
Agent, as a independent module, exchanges knowledge & information which are classified to their characteristics according to shared protocol. i.e. Agent Communication Language(AC1,). Fuzzy inference system represents the experiential knowledge as li~~guisticco ntrol rule and enables us to execute the knowledge using fuzzy inference. This study tries connecting fuzzy inference system with agent-based system and inspects applicability to hull form design through inferring principle dimension and hull form coefficients.
Modeling of Nonlinear Dynamic Dynamic Systems Using a Modified GMDH Algorithm
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 50~55
The GMDH(Group Method of Data Handling) is a useful data analysis technique for identification of nonlinear complex systems. Therefore, in this paper the application method of GMDH algorithm for modeling nonlinear dynamic systems is proposed. The identification of dynamic systems by using GMDH consists of applying a set of input/output data and computing the necessary coefficient set dynamically. Also, in this paper, by reducing sequentially the criterion which can adopt or reject the data, a method to prevent excessive computation that is a disadvantage of GMDH is proposed.
Automatic Fuzzy Rule Generation Using Neural Networks Based Reinforcement Larning
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 56~66
On Fuzzy Connectedness
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 67~70
If there exists a fuzzy continuous function from a fuzzy topological space (X, T) onto the discrete fuzzy toplogical space with two elements, then the space (X, T) is fuzzy disconnected. However, the converse is not true at all. We introduce an example for this and suggest a sufficient condition for which the converse holds.
Optimum Allocation of Pipe Support Using Combined Optimization Algorithm by Genetic Algorithm and Random Tabu Search Method
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 71~79
This paper introduces a new optimization algorithm which is combined with genetic algorithm and random tabu search method. Genetic algorithm is a random search algorithm which can find the global optimum without converging local optimum. And tabu search method is a very fast search method in convergent speed. The optimizing ability and convergent characteristics of a new combined optimization algorithm is identified by using a test function which have many local optimums and an optimum allocation of pipe support. The caculation results are compared with the existing genetic algorithm.
Online Fuzzy Modelling of Nonlinear Systems Using a Genetic Algorithm
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 80~87
This paper presents and online scheme for fuzzy modelling of nonlinear systems, based on the model adjustment technique and the genetic algorithm technique. The fuzzy model is characterized by fuzzy "if-then" rules which represent locally linear input-output relations whose consequence parts are defined as subsystems of a nonlinear sysem. The discrete-time model for each subsystem is obtained to deal with initalization and unmeasurable signal problems in online estimation and the final output of the fuzzy model is computed from the outputs of the discrete-time models. Then, the parameters of both the premise and consequence parts of the fuzzy model are adjusted by a genetic algorithm. A set of simulation works is carried out to demonstrate the effectiveness of the proposed method.ed method.
Initial smooth Fuzzy Topological Spaces
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 88~94
We will difine a base of a smooth fuzzy topological space and investigate some properties of bases. We will prove the existences of initial smooth fuzzy topological spaces. From this fact, we can define subspaces and products of smooth fuzzy topological spaces.
Recognition of Tactilie Image Dependent on Imposed Force Using Fuzzy Fusion Algorithm
Journal of Korean Institute of Intelligent Systems, volume 8, issue 3, 1998, Pages 95~103
This paper deals with a problem occuring in recognition of tactile images due to the effects of imposed force at a me urement moment. Tactile image of a contact surface, used for recognition of the surface type, varies depending on the forces imposed so that a false recognition may result in. This paper fuzzifies two parameters of the contour of a tactile image with the membership function formed by considering the imposed force. Two fuzzifed paramenters are fused by the average Minkowski's dist; lnce. The proposed algorithm was implemented on the multisensor system cnmposed of an optical tact le sensor and a 6 axes forceltorque sensor. By the experiments, the proposed algorithm has shown average recognition ratio greater than 869% over all imposed force ranges and object models which is about 14% enhancement comparing to the case where only the contour information is used. The pro- ~oseda lgorithm can be used for end-effectors manipulating a deformable or fragile objects or for recognition of 3D objects by implementing on multi-fingered robot hand.