• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.238-243
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• 2007

The TAM(Topographic Attentive Mapping) network neural network model is an especially effective one for pattern analysis. It is composed of of Input layer, category layer, and output layer. Fuzzy rule, lot input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of leadership type for organizational leader and show its usefulness. Here, criteria of input layer and target value of output layer are the value and leadership related personality type variables of the Egogram and Enneagram, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.86-93
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• 2006

The TAM(Topographic Attentive Mapping) network based on a biologically-motivated neural network model is an especially effective one for pattern analysis. It is composed of of input layer, category layer, and output layer. Fuzzy rule, for input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of core competency model for subcontractors of construction companies and show its usefulness.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.642-646
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• 2006

The fuzzy TAM(Topographical Attentive Mapping) network is a supervised method of pattern analysis which is composed of input layer, category layer, and output layer. But if we don't know the target value of the pattern, the network can not be trained. In this case, the target value can be replaced by a result induced by using an unsupervised neural network as the SOM (Self-organizing Map). In this paper, we apply the results of SOM to fuzzy TAM network and show its usefulness through the case study.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.622-626
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• 2006

In this paper, we show the usefulness of an methodology using a neural network that it analyzes a relation between learning personality related variables of the Enneargram and learning personality types. The Enneargram is a tool to classify learning personality types. In other words, we analyzed patterns of learning personality types-actaul-spontaneous type, actual-routine type, conceptual-specific type, conceptual-global type - by using the fuzzy TAM network that are very useful tool for pattern analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.620-623
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• 2003

The TAM (Topographic Attentive Mapping) network is a biologically-motivated neural network. Fuzzy rules are acquired from the TAM network by the pruning algorithm. In this paper we formulate a new input layer using Gabor function for TAU network to realize receptive field of human visual cortex.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.638-641
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• 2008

In this paper, a model to classify the method using the fuzzy TAM with preprocessing of data was developed. The preprocessing method can be divide the problem using the characteristics in the case of category type factor. In case of continuous type factor, if there was exist factor's range which is not overlapping by class, the data belong to the range was fixed and eliminated in classification. After these preprocessing of data, classified operation of Fuzzy TAM is performed.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.526-533
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• 2007

In this paper, we study to the pattern of job satisfaction using four theories of transactional analysis-egogram, life positions, strokes, time structuring-for organizational members. The tool of pattern analysis is used fuzzy TAM network which Is especially effective for pattern analysis. The input data of fuzzy TAM network ate values of four theories in transactional analysis, the output data is the classes which is divided by two groups from score of job satisfaction. From the result of this study, the correct rates of training data and checking data are 85-100% and 60%, respectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.338-341
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• 2003

TIn this study, by formulating input layer, category later, and output layer from data, and in using TAM(Topographic Attentive Mapping) network that created fuzzy rule, it categorized into companies went bankrupt with finances in the black figures, and in the red figures.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.54-61
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. By using the FCM, authors have proposed FCM-based fault diagnostic algorithm. However, it can offer multiple interpretations for a single fault. In process engineering, as experience accumulated, some form of quantitative process knowledge is available. If this information can be integrated into the FCM-based fault diagnosis, the diagnostic resolution can be further improved. The purpose of this paper is to propose an enhanced FCM-based fault diagnostic scheme. Firstly, the membership function of fuzzy set theory is used to integrate quantitative knowledge into the FCM-based diagnostic scheme. Secondly, modified TAM recall procedure is proposed. Considering that the integration of quantitative knowledge into FCM-based diagnosis requires a great deal of engineering efforts, thirdly, an automated procedure for fusing the quantitative knowledge into FCM-based diagnosis is proposed by utilizing self-learning feature of neural network. Finally, the proposed diagnostic scheme has been tested by simulation on the two-tank system.