• Korean Journal of Cognitive Science
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• v.6 no.1
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• pp.5-30
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• 1995

Fodor and his followers argue that connectionist model does not have compositional structure,or else it is merely an implementation of a classicist model.'Classical constityency'is used importantly to show connectionist model does not have compositional structure.However,in contrast Fodor and his followers,I maintain that it is possible for connectionist model to have compositional structure without attributing classical constituency to cognitive model,and the very classical consitiuency makes the connectionist model nonimplementational.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.331-338
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• 1992

The application of Connectionist expert system using neural network to fault diagnosis of power system is presented and compared with rule-based expert system. Also, the merits of Connectionist model using neural network is presented. In this paper, the neural network for fault diagnosis is hierarchically composed by 3 neural network classes. The whole power system is divided into subsystems, the neural networks (Class II) which take charge of each subsystem and the neural network (Class III) which connects subsystems are composed. Every section of power system is classified into one of the typical sections which can be applied with same diagnosis rules, as line-section, bus-section, transformer-section. For each typical section, only one neural network (Class I) is composed. As the proposed model has hierarchical structure, the great reduction of learning structure is achieved. With parallel distributed processing, we show the possibility of on-line fault diagnosis.

• The Journal of Society for e-Business Studies
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• v.7 no.1
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• pp.87-106
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• 2002

The behavioral and dynamic implications of an ERP implementation/installation are, to say the least, not well understood. Getting the switches set to enable the ERP software to go live is becoming straightforward. The really difficult part is understanding all of the dynamic interactions that accrue as a consequence. Dynamic causal and connectionist models are employed to facilitate an understanding of the dynamics and to enable control of the information-enhanced processes to take place. The connectionist model ran be analyzing (behind the scenes) the information accesses and transfers and coming If some conclusions about strong linkages that are getting established and what the behavioral implications of those new linkages and information accesses we. Ultimately, the connectionist model will come to an understanding of the dynamic, behavioral implications of the larger ERP implementation/installation per se. The underlying connectionist model will determine information transfers and workflow. Once a map of these two infrastructures is determined by the model, it becomes a relatively easy job for an analyst to suggest improvements in both. Connectionist models start with analog object structures and then use learning to produce mechanisms for managerial problem diagnoses. These mechanisms are neural models with multiple-layer structures that support continuous input/output. Based on earlier work performed and published by the author[10][11], a Connectionist ReasOning and LEarning System(CROLES) is developed that mimics the real-world reasoning infrastructure. Coupled with an explanation subsystem, this system can provide explanations as to why a particular reasoning structure behaved the way it did. Such a system operates in the backgmund, observing what is happening as every information access, every information response coming from each and every intelligent node (whether natural or artificial) operating within the ERP infrastructure is recorded and encoded. The CROLES is also able to transfer all workflows and map these onto the decision-making nodes of the organization.

• Korean Journal of Cognitive Science
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• v.7 no.4
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• pp.141-162
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• 1996

• Journal of Korean Elementary Science Education
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• v.23 no.3
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• pp.251-265
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• 2004

Conceptual teaming has been one of the important issue in science education, and its theory and method has been interacted with the studies of philosophy of science, cognitive science, and cognitive psychology. The last two decades have witnessed a remarkable growth of the study on brain-style computation, i.e. connectionism. This study aimed to investigate the properties of the connectionist views of the concept and its implications in the conceptual learning of science. In connectionist views, a concept was represented as a pattern of activity distributed over many connected units, and a kind of network composed of many sub-concept units. And the ＇distributed representation＇ had the features of the constructivity, the automatically generalization, and the tunability. On the base of these views, it was suggested that (ⅰ) ＇Typically-Perceived-Situation＇, a kind of mental representation rising spontaneously in an individual mind when someone is thinking about any object, should be highlighted, and (ⅱ) the roles of the sub-concept units in formation of concept and the resolution of concept into the sub-concept units should be highlighted. Finally the meanings of these implications in conceptual teaming of science are discussed.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.484-487
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• 1992

To resolve the knowledge acquisition bottleneck in the expert systems, the connectionist expert systems have been proposed, which facilitate learning capability of neural networks. This paper is to modify Gallant's connectionist expert network so that it can be applied to more general problems : 1) The hidden nodes are added between the input nodes and an output node, so that the back propagation learning algorithm is used instead of perception based Pocket algorithm. 2) Inference engine is thus modified by modeling that a node may have uncertainties due to unknown inputs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.243-248
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• 1996

In this paper, a general neural-network-based connectionist model, called Fuzzy Neural Network(FNN), is proposed for the realization of a fuzzy logic control system. The proposed FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Such FNN can be constructed from training examples by learning rule, and the connectionist structure can be trained to develop fuzzy logic rules and find optimal input/output membership functions. Computer simulation examples will be presented to illustrate the performance and applicability of the proposed FNN, and their associated learning algorithms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.410-412
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• 1998

Subsymbolic Knowledge processing is said to be changed states of networks constructed from small elements. subsymbolic systems also make it possible to use connectionist models for knowledge processing. Connectionist realization such modulus are modulus linked together for solving a given problem. We study using neural networks as distinct actions. The output vectors produced by the neural networks are consider as a new facts. These new facts are then processed to activate another networks or used in the current production rule, The production rule is applying knowledge stored in the knowledge base to make inference. After neural networks knowledge base is constructed and trained. We present a running sample of incorporating neural network knowledge base. We implement using rochester connectionist simulator. We suggest that incorporating neural network knowledge base. Therefore incorporated neural network knowledge base ensures a cleaner solution which results in better perfor s.

• Korean Journal of Biological Psychiatry
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• v.4 no.2
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• pp.194-197
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• 1997

Neural network models, also known as connectionist models or PDP models, simulate some functions of the brain and may promise to give insight in understanding the cognitive brain functions. The models composed of neuron-like elements that are linked into circuits can learn and adapt to its environment in a trial and error fashion. In this article, the history and principles of the neural network modeling are briefly reviewed, and its applications to psychiatry are discussed.

• Proceedings of the Korean Society for Language and Information Conference
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• 1992.02a
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• pp.90-98
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• 1992