• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.67-68
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• 2007

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.707-712
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• 1998

This paper presents several softcomputing techniques such as neural networks, fuzzy logic and genetic algorithms : Neural networks as brain metaphor provide fundamental structure, fuzzy logic gives a possibility to utilize top-down knowledge from designer, and genetic algorithms as evolution metaphor determine several system parameters with the process of bottom up development. With these techniques, we develop a pattern recognizer which consists of multiple neural networks aggregated by fuzzy integral in which genetic algorithms determine the fuzzy density values. The experimental results with the problem of recognizing totally unconstrained handwritten numeral show that the performance of the proposed method is superior to that of conventional methods.

• Journal of Navigation and Port Research
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• v.28 no.6
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• pp.541-548
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• 2004

In the maritime container terminal. LMCTS (Linear Motor-based Container Transfer System) is horizontal transfer system for the yard automation, which In., been proposed to take the place of AGV (Automated Guided Vehicle). The system is based on PMLSM (Permanent Magnetic Linear Synchronous Motor) that is consists of stator modules on the rail and shuttle car (mover). Because of large variant of mover's weight by loading and unloading containers, the difference of each characteristic of stator modules, and a stator module's trouble etc. LMCTS is considered as that the system is changed its model suddenly and variously. In this paper, we will introduce the softcomputing method of a multi-step prediction control for LMCTS using DR- FNN (Dynamically-constructed Recurrent Fuzzy Neural Network). The proposed control system is used two networks for multi step prediction Consequently, the system has an ability to adapt for external disturbance, detent force, force ripple, and sudden changes by loading and unloading the container.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.27-34
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• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.