• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.13-17
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• 2024

In this paper, union-based rule antecedent neuro-fuzzy controller, which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The proposed neuro-fuzzy controller allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the proposed neuro-fuzzy controller, we consider the multiple-term unified logic processor (MULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, the genetic algorithm (GA) constructs a Boolean skeleton of the proposed neuro-fuzzy controller, while the stochastic reinforcement learning refines the binary connections of the GA-optimized controller for further improvement of the performance index. An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation and experiment.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.51-55
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• 2020

In this paper, an improved FAM is proposed by adopting similarity learning in the existing FAM (Fuzzy Associative Memory) used in image restoration. Image restoration refers to the recovery of the latent clean image from its noise-corrupted version. In serious application like face recognition, this process should be noise-tolerant, robust, fast, and scalable. The existing FAM is a simple single layered neural network that can be applied to this domain with its robust fuzzy control but has low capacity problem in real world applications. That similarity measure is implied to the connection strength of the FAM structure to minimize the root mean square error between the recovered and the original image. The efficacy of the proposed algorithm is verified with significant low error magnitude from random noise in our experiment.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1312-1320
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• 2003

In this paper, we consider a multilayer neural network, with a single hidden layer. Error backpropagation learning method used widely in multilayer neural networks has a possibility of local minima due to the inadequate weights and the insufficient number of hidden nodes. So we propose a fuzzy supervised learning algorithm by using self-generation that self-generates hidden nodes by the compound fuzzy single layer perceptron and modified ART1. From the input layer to hidden layer, a modified ART1 is used to produce nodes. And winner take-all method is adopted to the connection weight adaptation, so that a stored pattern for some pattern gets updated. The proposed method has applied to the student identification card images. In simulation results, the proposed method reduces a possibility of local minima and improves learning speed and paralysis than the conventional error backpropagation learning algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.353-356
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• 1997

Vehicle steering system determines the direction of a vehicle. A manual steering system consists of mechanical connections between the steering wheel and tires. Recent power steering system adds an actuator to help a driver to steer easily at low speed. However, at front collision, the driver can be injured by steering shaft and the power steering pump decreases the engine power. To solve these problems, electronic power steering system which connects the steering wheel and tires with electronic connection is proposed, that has advantages such as decrease of engine load and increase of driver safety reactive. Since the ratio between driver's steering torque and steering torque of tires can be controlled freely, the torque which is delivered from the road to the driver through tires and steering wheel can be reshaped to make the driver feel comfortable. In this paper, the ratio of delivering steering torque and the magnitude of force to be delivered from road to driver has been controlled using fuzzy controller, and it's effectiveness has been shown through simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.772-777
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• 1993

This paper presents a neuro-fuzzy modelling method that determines chemicals dosing model based on historical operation data for effective water quality control in water treatment system and calculates automatically the amount of optimum chemicals dosing against the changes of raw water qualities and flow rate. The structure identification in the modelling by means of neuro-fuzzy reasing is performed by Genetic Algorithm(GA) and Complex Method in which the numbers of hidden layer and its hidden nodes, learning rate and connection pattern between input layer and output layer are identified. The learning network is implemented utilizing Back Propagation(BP) algorithm. The effectiveness of the proposed modelling scheme and the feasibility of the acquired neuro-fuzzy network is evaluated through computer simulation for chemicals dosing control in water treatment system.

• Proceedings of the KIEE Conference
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• 2006.07e
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• pp.67-68
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• 2006

In this paper, we introduced about safety algorithm of transformer for MV/LV distribution customers using by fuzzy theory. Overload of transformer becomes different by surrounding temperature. And parameters about overload of transformer are connection each other. Therefore, we organize safety algorithm consider overload of transformer and surrounding temperature in this research. And we induce the relational expression of each parameters using experiment data of IEEE std C57.91-1995. Deduction of result used fuzzy reasoning. We guess the safety algorithm suggested in this paper shows the new direction that heavy electrical equipments including switchboard are going to develop in the future.

• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.201-208
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• 2000

In this paper We describe an algorithm which is devised for 4he partition o# the input space and the generation of fuzzy rules by the fuzzy entropy and tested with the time series prediction problem using Mackey-Glass chaotic time series. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rules base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. The Proposed algorithm has been naturally derived by means of the synergistic combination of the approximative approach and the descriptive approach. Each output of the rule's consequences has expressed with its connection weights in order to minimize the system parameters and reduce its complexities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.683-691
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• 2000

In this paper, we proposed the cell multiplexer using Hopfield neural network and the bandwidth predictor using the backpropagation neural network in order to make an accurate call setup decision. The cell multiplexer controls heterogeneous traffic and the bandwidth predictor estimates minimum bandwidth which satisfies traffic's QoS and maximizes throughput in network. Also, a novel connection admission controller decides on connection setup using the predicted bandwidth from bandwidth predictor and available bandwidth in networks. And then, we proposed a fuzzy traffic policer, when traffic sources violate the contract, takes an appropriate action and aim proved traffic shaper, which controls burstness which is one of key characteristics in multimedia traffic. We simulated the proposed controller. Simulation results show that the proposed controller outperforms existing controller.

• Journal of Environmental Impact Assessment
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• v.14 no.6
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• pp.387-402
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• 2005

In this study, we tried to predict the change of future land-cover and relationships between land-cover change and geo-spatial information in the Gongju area by using fuzzy logic operation. Quantitative evaluation of prediction models was carried out using a prediction rate curve using. Based on the analysis of correlations between the geo-spatial information and land-cover change, the class with the highest correlation was extracted. Fuzzy operations were used to predict land-cover change and determine the land-cover prediction maps that were the most suitable. It was predicted that in urban areas, the urban expansion of old and new towns would occur centering on the Gem-river, and that urbanization of areas along the interchange and national roads would also expand. Among agricultural areas, areas adjacent to national roads connected to small tributaries of the Gem-river and neighboring areas would likely experience changes. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the possibility of forest damage is very high. As a result of validation using the prediction rate curve, it was indicated that among fuzzy operators, the maximum fuzzy operator was the most suitable for analyzing land-cover change in urban and agricultural areas. Other fuzzy operators resulted in the similar prediction capabilities. However, in the prediction rate curve of integrated models for land-cover prediction in the forest areas, most fuzzy operators resulted in poorer prediction capabilities. Thus, it is necessary to apply new thematic maps or prediction models in connection with the effective prediction of changes in the forest areas.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.59-65
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• 2020

The Internet of Things (IoT) era, in which all items used in daily life are equipped with a network connection function, and they are closely linked to increase the convenience of life and work, has opened wide. Robots also need to develop according to the IoT environment. A use of new type of evolved fuzzy machine (EFM) for generating legged robot trajectory in IoT enviornmentms is discussed in this paper. Fuzzy system has been widely used for describing nonlinear systems. In fuzzy system, determination of antecedent and consequent structures of fuzzy model has been one of the most important problems. EFM is described which carries out evolving antecedent and consequent structure of fuzzy system for legged robot. To generate the robot trajectory, parameters of each structure in the fuzzy system are tuned automatically by the EFM. The results demonstrate the performance of the proposed approach for the legged robot.