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3D reconstruction and face recognition using kernel-based ICA and neural networks

EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 5406-5415页

作者： Kuo, Shye-Chorng Lin, Cheng-Jian Liao, Jan-Ray Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taiping 411 Taichung County Taiwan Natl Chung Hsing Univ Dept Elect Engn Taichung 402 Taiwan

Kernel-based nonlinear characteristic extraction and classification algorithms are popular new research directions in machine learning. In this paper, we propose an improved photometric stereo scheme based on improved kernel-independent component analysis method to reconstruct 3D human faces. Next, we fetch the information of 3D faces for facial face recognition. For reconstruction, we obtain the correct normal vector's sequence to form the surface, and use a method for enforcing integrability to reconstruct 3D objects. We test our algorithm on a number of real images captured from the Yale Face Database B. and use three kinds of methods to fetch characteristic values. Those methods are called contour-based, circle-based, and feature-based methods. Then, a three-layer, feed-forward neural network trained by a back-propagation algorithm is used to realize a classifier. All the experimental results were compared to those of the existing human face reconstruction and recognition approaches tested on the same images. The experimental results demonstrate that the proposed improved kernel independent component analysis (IKICA) method is efficient in reconstruction and face recognition applications. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Independent component analysis 3D human face reconstruction 3D human face recognition back-propagation algorithm Neural networks

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Artificial neural network analysis of the performance characteristics of a reversibly used cooling tower under cross flow conditions for heat pump heating system in winter

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ENERGY AND BUILDINGS 2011年第7期43卷 1685-1693页

作者： Wu, Jiasheng Zhang, Guoqiang Zhang, Quan Zhou, Jin Wang, Yu Hunan Univ Coll Civil Engn Changsha 410082 Hunan Peoples R China

This study deals with predicting the performance characteristics of a reversibly used cooling tower (RUCT) under cross flow conditions for heat pump heating system in winter using artificial neural network (ANN) technique. For this aim, extensive field experimental work has been carried out in order to gather enough data for training and prediction. After back-propagation (BP) training combined with principal component analysis, the three-layer ANN model with a tangent sigmoid transfer function at hidden layer with 11 neurons and a linear transfer function at output layer was obtained. The predictions agreed well with the experimental values with a satisfactory correlation coefficient in the range of 0.9249-0.9988, the absolute fraction of variance in the range of 0.8753-0.9976, and the mean relative error in the range of 0.0008-0.54%, moreover, the root mean square error values for the ANN training and predictions were very low relative to the range of the experiments. The results reveal that ANN model can be used effectively for predicting the performance characteristics of RUCT under cross flow conditions, then providing the theoretical basis on the research of heat and mass transfer inside RUCT, which is important for design and running control of the RUCT system. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Reversibly used cooling tower Heating Artificial neural network back-propagation algorithm

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Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

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PRAMANA-JOURNAL OF PHYSICS 2011年第6期76卷 887-900页

作者： Kadir, Abdurahman Wang, Xing-Yuan Zhao, Yu-Zhang Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China Xinjiang Univ Finance & Econ Sch Engn & Comp Sci Urumqi 830012 Peoples R China

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.

关键词： Chaos control adaptive control adaptive identifier fuzzy neural network back-propagation algorithm

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Development of a predictive system for car fuel consumption using an artificial neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 4967-4971页

作者： Wu, Jian-Da Liu, Jun-Ching Natl Changhua Univ Educ Grad Inst Vehicle Engn Changhua 500 Taiwan

A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors which will effect the fuel consumption of a car in a practical drive procedure, however, in the present system the impact factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In the fuel consumption forecasting, to verify the effect of the proposed predictive system, an artificial neural network with back-propagation neural network has a learning capability for car fuel consumption prediction. The prediction results demonstrated that the proposed system using neural network is effective and the performance is satisfactory in fuel consumption prediction. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Fuel consumption Artificial neural network back-propagation algorithm

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Performance evaluation of open-cycle solar regenerator using artificial neural network technique

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ENERGY AND BUILDINGS 2011年第2-3期43卷 454-457页

作者： Aly, Ayman A. Zeidan, El-Shafei B. Hamed, Ahmed M. Taif Univ Dept Mech Engn Fac Engn Al Hawyah Saudi Arabia

Theoretical investigation on the performance of lithium chloride (LiCl) absorption cooling system using an artificial neural network (ANN) model is presented. Tabulated data from the literature are used to construct the ANN model. Solar collector desiccant/regenerator is applied to re-concentrate the working solution. Using the proposed model, the effect of system design parameters;namely regenerator length, and air flow rate on the performance of the system is demonstrated. The variation of the thermo-physical parameters along the regenerator length is highlighted. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Solar energy Open-cycle Regeneration Lithium chloride Absorption Liquid desiccant Neural network back-propagation algorithm

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Fault diagnosis on bottle filling plant using genetic-based neural network

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ADVANCES IN ENGINEERING SOFTWARE 2011年第12期42卷 1051-1058页

作者： Demetgul, M. Unal, M. Tansel, I. N. Yazicioglu, O. Marmara Univ Dept Mechatron Engn Fac Technol Istanbul Turkey Marmara Univ Dept Elect Comp Educ Tech Educ Fac Istanbul Turkey Florida Int Univ Dept Mech Engn Miami FL 33174 USA ITICU Dept Ind Engn Istanbul Turkey

Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA-ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of the proposed system was evaluated by using experimental data collected at a pneumatic work cell which attach caps to the bottles. The sensory data was collected at normal operating conditions and a series of faults were imposed to the system such as missing bottle, attaching nonworking bottle caps at two different cylinders, two air pressure problems (insufficient and low air), and not filling water. The study demonstrated the convenience, accuracy and speed of the proposed GA-NN environment. It may also be used for training for selection of ANN configurations at various applications. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Neural network Genetic algorithm Bottle filling plant Pneumatic Fault diagnosis back-propagation algorithm

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Statistical and evolutionary optimisation of operating conditions for enhanced production of fungal L-asparaginase

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CHEMICAL PAPERS 2011年第6期65卷 798-804页

作者： Baskar, Gurunathan Renganathan, Sahadevan Anna Univ Alagappa Coll Technol Dept Chem Engn Madras 600025 Tamil Nadu India St Josephs Coll Engn Dept Biotechnol Madras 600119 Tamil Nadu India

A three-level central composite design of the Response Surface Methodology and the Artificial Neural Network-linked Genetic algorithm were applied to find the optimum operating conditions for enhanced production of L-asparaginase by the submerged fermentation of Aspergillus terreus MTCC 1782. The various effects of the operating conditions, including temperature, pH, inoculum concentration, agitation rate, and fermentation time on the experimental production of L-asparaginase, were fitted to a second-order polynomial model and non-linear models using Response Surface Methodology and the Artificial Neural Network, respectively. The Artificial Neural Network model fitted well, achieving a higher coefficient of determination (R-2 = 0.999) than the second-order polynomial model (R-2 = 0.962). The L-asparaginase activity (38.57 IU mL(-1)) predicted under the optimum conditions of 32.08 degrees C, pH of 5.85, inoculum concentration of 1 vol. %, agitation rate of 123.5 min(-1), and fermentation time of 55.1 h was obtained using the Artificial Neural Network-linked Genetic algorithm in very close agreement with the activity of 37.84 IU mL(- 1) achieved in confirmation experiments. (C) 2011 Institute of Chemistry, Slovak Academy of Sciences

关键词： L-asparaginase optimisation operating conditions response-surface methodology back-propagation algorithm genetic algorithm

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A Design Methodology for Energy Aware Neural Networks

A Design Methodology for Energy Aware Neural Networks

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7th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Dabbagh, Mehiar Hajj, Hazem Chehab, Ali El-Hajj, Wassim Kayssi, Ayman Mansour, Mohammad Amer Univ Beirut Dept Elect & Comp Engn Beirut Lebanon

ISBN: (纸本)9781424495375

The increasing demand for mobile devices and high performance computing has made energy consumption a main issue in computer technology. Mobile devices require extended battery life, but the available technology still puts limits on the need for recharging the devices. High performance computing has a high price tag on energy for compute-intensive applications such as data mining. As a result, optimizations at various layers of the computer platform are becoming necessary to minimize energy usage or extend the time before a battery needs to be recharged. This paper focuses on back-propagation neural network algorithm, one of the popular compute-intensive data mining algorithms. The goal is to present a design methodology for developing an energy aware algorithm. The key idea revolves around identifying operations called kernels, which are frequently used in the algorithm, and that can be implemented in hardware. Optimizing these kernels for performance or energy would then lead to a major impact in these areas. These kernels are analyzed for their impact on the overall application energy using energy-based asymptotic analysis. The methodology then considers additional optimizations not related to kernels, but are specific to the back-propagation algorithm. Suggestions are provided to improve the performance and reduce energy consumption. Experiments show that there are significant potentials in energy reduction through the use of alternative lower energy kernels or through custom optimizations with tradeoffs in the accuracy of the results.

关键词： Data Mining Neural Networks back-propagation algorithm Energy Aware

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DESIGN OF RECURRENT FUZZY NEURAL NETWORK AND GENERAL REGRESSION NEURAL NETWORK CONTROLLER FOR TRAVELING-WAVE ULTRASONIC MOTOR

DESIGN OF RECURRENT FUZZY NEURAL NETWORK AND GENERAL REGRESS...

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International Conference on Neural Computation Theory and Applications

作者： Chen, Tien-Chi Ren, Tsai-Jiun Lou, Yi-Wei Kun Shan Univ Dept Elect Engn Tainan Taiwan Kun Shan Univ Dept Informat Engn Tainan Taiwan

ISBN: (纸本)9789898425843

The traveling-wave ultrasonic motor (TWUSM) has significant features such as high holding torque at low speed range, high precision, fast dynamics, simple structure, no electromagnetic interference. The TWUSM has been used in many practical areas such as industrial, medical, robotic, and automotive applications. However, the dynamic model of the TWUSM motor has the nonlinear characteristic and dead-zone problem which varies with many driving conditions. This paper presents a novel control scheme, recurrent fuzzy neural network (RFNN) and general regression neural network (GRNN) controller, for a TWUSM control. The RFNN provides a real-time control such that the TWUSM output can track the reference command. The back-propagation algorithm is applied in the RFNN to automatically adjust the parameters on-line. The adaptive laws of the RFNN are derived by Lyapunov theorem such that the stability of the system can be absolute. The GRNN controller is appended to the RFNN controller to compensate the dead-zone of the TWUSM system using a predefined set. The experimental results are provided to demonstrate the effectiveness of the proposed controller.

关键词： Traveling-wave ultrasonic motor TWUSM Recurrent fuzzy neural network RFNN back-propagation algorithm Lyapunov theorem General regression neural network GRNN Dead-zone

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Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第9期37卷 6233-6239页

作者： Khashman, Adnan Near East Univ Intelligent Syst Res Grp TR-10 Lefkosa Mersin Turkey

This paper describes a credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm. We train and implement three neural networks to decide whether to approve or reject a credit application. Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. The neural networks are trained using real world credit application cases from the German credit approval datasets which has 1000 cases;each case with 24 numerical attributes;based on which an application is accepted or rejected. Nine learning schemes with different training-to-validation data ratios have been investigated, and a comparison between their implementation results has been provided. Experimental results will suggest which neural network model, and under which learning scheme, can the proposed credit risk evaluation system deliver optimum performance;where it may be used efficiently, and quickly in automatic processing of credit applications. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Credit risk evaluation Artificial neural networks (ANN) back-propagation algorithm Neural learning schemes Finance and banking

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