• The Korean Journal of Ecology
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• 제23권2호
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• pp.89-100
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• 2000

On community data. sampled in regular intervals on a long-term basis. artificial neural networks were implemented to extract information on characterizing patterns of community changes. The Adaptive Resonance Theory and Kohonen Network were both utilized in learning benthic macroinvertebrate communities in the Soktae Stream of the Suyong River collected monthly for three years. Initially, by regarding each monthly collection as a separate sample unit, communities were grouped into similar patterns after training with the networks. Subsequently, changes in communities in a sequence of samplings (e.g., two-month, four-month, etc.) were given as input to the networks. After training, it was possible to recognize new data set in line with the sampling procedure. Through the comparative study on benthic macroinvertebrates with these learning processes, patterns of community changes in chironomids diverged while those of the total benthic macro-invertebrates tended to be more stable.

• 상하수도학회지
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• 제19권4호
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• pp.395-403
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• 2005

It is very important to forecast water supply for reasonal operation and management of water utilities. In this paper, water supply forecasting models using artificial intelligence are developed. Artificial intelligence models shows better results by using Temperature(t), water supply discharge (t-1) and water supply discharge (t-2), which are expressed by neural network(LMNNWS; Levenberg-Marquardt Neural Network for Water Supply, MDNNWS; MoDular Neural Network for Water Supply) and neuro fuzzy(ANASWS; Adaptive Neuro-Fuzzy Inference Systems for Water Supply). ANFISWS model which is applied for water supply forecasting shows stable application to the variable water supply data. As results, MDNNWS model shows the highest overall accuracy among proposed water supply forecasting models and the lowest estimation error with the order of ANFISWS, LMNNWS model.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권4호
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• pp.220-227
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• 2005

This paper presents hybrid artificial intelligent(HAI) controller based on the vector controlled IPMSM drive system. And it is based on artificial technologies that adaptive neural network fuzzy(A-NNF) is to speed control and artificial neural network(ANN) is to speed estimation. The salient feature of this technique is the HAI controller The hybrid action tolerates any inaccuracies in the fuzzy logic assignment rules or in the neural network stationary weights. Speed estimators using feedforward multilayer and artificial neural network(ANN) are compared. The back-propagation algorithm is easy to derived the estimated speed tracks precisely the actual motor speed. This paper presents the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.931-934
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• 2005

This paper describes an adaptive recommendation system that provides real-time personalized trading advice to the investors based on their profiles and trading information environment. A proposed system integrates Stochastic technical analysis and artificial neural network that incorporates an adaptive user modeling. The user model is constructed and updated based on initial user profile and recorded user interactions with the system. The information presented to each individual user is also tailor-made to fit the user's behavior and preference. A system prototype was implemented in JAVA. Experiments used to evaluate the system's performance were done on both human subjects and synthetic users. The results show our proposed system is able to rapidly learn to provide appropriate advice to different types of users.

• Nuclear Engineering and Technology
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• 제48권6호
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• pp.1315-1320
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• 2016

The present study aims to predict the heat transfer characteristics around a square cylinder with different corner radii using multivariate adaptive regression splines (MARS). Further, the MARS-generated objective function is optimized by particle swarm optimization. The data for the prediction are taken from the recently published article by the present authors [P. Dey, A. Sarkar, A.K. Das, Development of GEP and ANN model to predict the unsteady forced convection over a cylinder, Neural Comput. Appl. (2015) 1-13]. Further, the MARS model is compared with artificial neural network and gene expression programming. It has been found that the MARS model is very efficient in predicting the heat transfer characteristics. It has also been found that MARS is more efficient than artificial neural network and gene expression programming in predicting the forced convection data, and also particle swarm optimization can efficiently optimize the heat transfer rate.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.176-178
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• 2004

Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of speed estimation and control for IPMSM using hybrid intelligent control. The hybrid combination of neural network and adaptive fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using adaptive neural network fuzzy(A-NNF) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed.

• Smart Structures and Systems
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• 제5권5호
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• pp.507-515
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• 2009

The present work is an alternative methodology in order to balance a nonlinear highly flexible rotor by using neural networks. This procedure was developed aiming at improving the performance of classical balancing methods, which are developed in the context of linearity between acting forces and resulting displacements and are not well adapted to these situations. In this paper a fully experimental procedure using neural networks is implemented for dealing with the adaptive balancing of nonlinear rotors. The nonlinearity results from the large displacements measured due to the high flexibility of the foundation. A neural network based meta-model was developed to represent the system. The initialization of the learning procedure of the network is performed by using the influence coefficient method and the adaptive balancing strategy is prone to converge rapidly to a satisfactory solution. The methodology is tested successfully experimentally.

• KIEAE Journal
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• 제14권3호
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• pp.97-103
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• 2014

This study aimed at developing integrated logic for controlling heating device and openings of the double skin facade buildings. Two major logics were developed-rule-based control logic and artificial neural network based control logic. The rule based logic represented the widely applied conventional method while the artificial neural network based logic meant the optimal method. Applying the optimal method, the predictive and adaptive controls were feasible for supplying the advanced thermal indoor environment. Comparative performance tests were conducted using the numerical computer simulation tools such as MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation). Analysis on the test results in the test module revealed that the artificial neural network-based control logics provided more comfortable and stable temperature conditions based on the optimal control of the heating device and opening conditions of the double skin facades. However, the amount of heat supply to the indoor space by the optimal method was increased for the better thermal conditioning. The number of on/off moments of the heating device, on the other hand, was significantly reduced. Therefore, the optimal logic is expected to beneficial to create more comfortable thermal environment and to potentially prevent system degradation.

• 제어로봇시스템학회논문지
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• 제5권8호
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• pp.898-906
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• 1999

A control structure is introduced for the purpose of rejecting periodic (or repetitive) disturbances on a tracking system. The objective of the proposed structure is to drive the output of the system to the reference input that will result in perfect following without any changing the inner configuration of the system. The structure includes an adaptation block which learns the dynamics of the periodic disturbance and forces the interferences, caused by disturbances, on the output of the system to be reduced. Since the control structure acquires the dynamics of the disturbance by on-line adaptation, it is possible to generate control signals that reject any slowly varying time-periodic disturbance provided that its amplitude is bounded. The artificial neural network is adopted as the adaptation block. The adaptation is done at an on-line process. For this , the real-time recurrent learning (RTRL) algoritnm is applied to the training of the artificial neural network.

• Geomechanics and Engineering
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• 제3권4호
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• pp.285-290
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• 2011

This article employs Multivariate Adaptive Regression Spline (MARS) for determination of friction capacity of driven piles in clay. MARS is non-parametric adaptive regression procedure. Pile length, pile diameter, effective vertical stress, and undrained shear strength are considered as input of MARS and the output of MARS is friction capacity. The developed MARS gives an equation for determination of $f_s$ of driven piles in clay. The results of the developed MARS have been compared with the Artificial Neural Network. This study shows that the developed MARS is a robust model for prediction of $f_s$ of driven piles in clay.