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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Access type: Open accessSubject: search.filters.subject.artificial neural network

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Now showing 1 - 8 of 8
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    An Augmented Reality Question Answering System Based on Ensemble Neural Networks
    (2017-01-01)
    Chen, Chi-Hua
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    Wu, Chen-Ling
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    Lo, Chi-Chun
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    Hwang, Feng-Jang
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    資訊管理與財務金融系 註：原資管所+財金所
    This paper proposes a classification algorithm based on ensemble neural networks. In the training phase, the proposed algorithm uses a random number of training data to develop multiple random artificial neural network (ANN) models until those ANN models converge. Those models with lower accuracy than the threshold are filtered out. The remaining highly accurate models will be used to predict the output in the testing phase. Meanwhile, the accuracy of ANN models is presented as a weighting value in the testing phase. In the testing phase, the testing data are loaded into the selected ANN models to predict the output class. The output values are multiplied by the corresponding weighting values of ANN models. Then the weighted average of the outputs can be obtained. Finally, the predicted output is converted into the predicted class. We design an augmented reality question answering system (AR-QAS) applying and implementing the proposed algorithm on mobile devices. AR-QAS offers an interactive user interface and automatically replies according to user's queries. By comparing with the logistic regression method and the ANN method, the experiment results demonstrate that the proposed algorithm offers the highest accuracy.
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    Developing a Conjunctive Use Optimization Model for Allocating Surface and Subsurface Water in an Off-Stream Artificial Lake System
    (2016-08-01)
    Pan, Chen-Che
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    Chen, Yu-Wen
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    Chang, Liang-Cheng
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    Huang, Chun-Wei
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    土木工程學系
    This work develops a rule curve-based conjunctive use management model for optimizing the operating rules for a lake-groundwater system with off-stream storage lakes. The proposed procedure is a simulation-optimization approach that embeds an Artificial Neural Network ( ANN) instead of a groundwater numerical model into a genetic algorithm ( GA). The direct physical exchange between lake water with groundwater is simulated using the ANN model, which is a reduced version of a full numerical model, MODFLOW with an LAK3 module. By applying the ANN model, the proposed procedure can reduce the computational burden that is induced by the nonlinear exchange. An operating rule-based optimal conjunctive use management model for the Gaopin Artificial lakes system in Taiwan was thus developed using the proposed framework. A set of optimal solutions involves rule curves and a discount ratio. Simulation results demonstrate that the embedded ANN model can accurately simulate the nonlinear exchange of a lake with groundwater. The embedded ANN model is less computationally complex than the numerical model. This work demonstrates a methodology for reducing the computational burden of the optimal conjunctive use management model that is associated with an internal nonlinear system by using the ANN reduced model. Specifically, the concept of, and results obtained using the developed operating rule-based model incorporating five artificial lakes and considering the nonlinear exchange of those lakes with the groundwater system provides a valuable practical reference for solving related conjunctive use problems.
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    Robot control optimization using reinforcement learning
    (1998-03-01)
    Song, KT
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    Sun, WY
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    電控工程研究所
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    Institute of Electrical and Control Engineering
    Conventional robot control schemes are basically model-based methods. However, exact modeling of robot dynamics poses considerable problems and faces various uncertainties in task execution. This paper proposes a reinforcement learning control approach for overcoming such drawbacks. An artificial neural network (ANN) serves as the learning structure, and an applied stochastic real-valued (SRV) unit as the learning method. Initially, force tracking control of a two-link robot arm is simulated to verify the control design. The simulation results confirm that even without information related to the robot dynamic model and environment states, operation rules for simultaneous controlling force and velocity are achievable by repetitive exploration. Hitherto, however, an acceptable performance has demanded many learning iterations and the learning speed proved too slow for practical applications. The approach herein, therefore, improves the tracking performance by combining a conventional controller with a reinforcement learning strategy. Experimental results demonstrate improved trajectory tracking performance of a two-link direct-drive robot manipulator using the proposed method.
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    Applying robust multi-response quality engineering for parameter selection using a novel neural-genetic algorithm
    (2003-01-01)
    Li, TS
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    Su, CT
    ;
    Chiang, TL
    ;
    工業工程與管理學系
    ;
    Department of Industrial Engineering and Management
    This study presents a neural-genetic algorithm to solve the selection problem of manufacturing process parameters. The proposed algorithm is a combination of artificial neural network (ANN) and genetic algorithms (GAs). In addition, the neural network is used to formulate a fitness function for predicting the value of the response based on the parameter settings. GAs then take the fitness function from the trained neural network to search for the optimal parameter combination. Owing to the most of manufactured products have more than one quality characteristic and the quality characteristics are generally correlated with each other, this study also proposes a desirability function to obtain a compromise, composite solution. A case study of how the silicon manufacturing process parameters are selected offline demonstrates the effectiveness of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.
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    Due-date assignment in wafer fabrication using artificial neural networks
    (2004-05-01)
    Sha, DY
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    Hsu, SY
    ;
    工業工程與管理學系
    ;
    Department of Industrial Engineering and Management
    Due-date assignment (DDA) is the first important task of shop floor control in wafer fabrication. Due-date related performance is impacted by the quality of the DDA rules. Assigning order due dates and timely delivering the goods to the customer will enhance customer service and competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN) prediction is considered in this work. An ANN-based DDA rule combined with simulation technology and statistical analysis is developed. Besides, regression-based DDA rules for wafer fabrication are modelled as benchmarking. Whether neural networks can outperform conventional and regression-based DDA rules taken from the literature is examined. From the simulation and statistical results, ANN-based DDA rules perform a better job in due-date prediction. ANN-based DDA rules have a lower tardiness rate than the other rules. ANN-based DDA rules have better sensitivity and variance than the other rules. Therefore, if the wafer fab information is not difficult to obtain, the ANN-based DDA rule can perform better due-date prediction. The SFM_sep and JIQ in regression-based and conventional rules are better than the others.
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    Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method
    (2005-06-16)
    Chiou, YC
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    Lan, LW
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    運輸與物流管理系 註：原交通所+運管所
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    Department of Transportation and Logistics Management
    Logic rules and membership functions are two key components of a fuzzy logic controller (FLC). If only one component is learned, the other one is often set subjectively thus can reduce the applicability of FLC. If both components are learned simultaneously, a very long chromosome is often needed thus may deteriorate the learning performance. To avoid these shortcomings, this paper employs genetic algorithms to learn both logic rules and membership functions sequentially. We propose a bi-level iterative evolution algorithm in selecting the logic rules and tuning the membership functions for a genetic fuzzy logic controller (GFLC). The upper level is to solve the composition of logic rules using the membership functions tuned by the lower level. The lower level is to determine the shape of membership functions using the logic rules learned from the upper level. We also propose a new encoding method for tuning the membership functions to overcome the problem of too many constraints. Our proposed GFLC model is compared with other similar GFLC, artificial neural network and fuzzy neural network models, which are trained and validated by the same examples with theoretical and field-observed car-following behaviors. The results reveal that our proposed GFLC has outperformed. © 2004 Elsevier B.V. All rights reserved.
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    Development of an artificial neural network to predict lead frame dimensions in an etching process
    (2006-02-01)
    Liu, TC
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    Li, RK
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    Chen, MC
    ;
    工業工程與管理學系
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    Department of Industrial Engineering and Management
    The electronic industry is rapidly developing, creating high demand for IC production. Etched semiconductor lead frames are the basic material used in IC packaging. IC packaging requires high-precision lead frames. The dimensions of the pilot hole are generally required to be highly precise in lead frame manufacturing. The photo-etching process must control the dimension of the pilot hole and record the manufacturing data of the etching machine and inspection data. This study presents the development of an artificial neural network (ANN) model that can be applied to construct the predicting model. The predictive model can estimate the dimensions of the pilot hole and thus determine the process parameters needed to improve lead frame quality in the etching process.
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    Forecasting electricity market pricing using artificial neural networks
    (2007-03-01)
    Pao, Hsiao-Tien
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    管理科學系
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    Department of Management Science
    Electricity price forecasting is extremely important for all market players, in particular for generating companies: in the short term, they must set up bids for the spot market; in the medium term, they have to define contract policies; and in the long term, they must define their expansion plans. For forecasting long-term electricity market pricing, in order to avoid excessive round-off and prediction errors, this paper proposes a new artificial neural network (ANN) with single output node structure by using direct forecasting approach. The potentials of ANNs are investigated by employing a rolling cross validation scheme. Out of sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are a more robust multi-step ahead forecasting method than autoregressive error models. Moreover, ANN predictions are quite accurate even when the length of the forecast horizon is relatively short or long. (c) 2006 Elsevier Ltd. All rights reserved.