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Author's reply and revision for time-varying weights

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1997年第3期8卷 813-814页

作者： Lee, KY Department of Electrical Engineering Pennsylvania State University PA USA

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A retrospective analysis of the utility of an artificial neural network to predict ED volume

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AMERICAN JOURNAL OF EMERGENCY MEDICINE 2014年第6期32卷 614-617页

作者： Menke, Nathan Benjamin Caputo, Nicholas Fraser, Robert Haber, Jordana Shields, Christopher Menke, Marie Nam Univ Pittsburgh Med Ctr Dept Emergency Med Div Med Toxicol Pittsburgh PA USA Lincoln Med & Mental Hlth Ctr Dept Emergency Med Bronx NY 10451 USA Univ Pittsburgh Med Ctr Dept Obstet Gynecol & Reprod Sci Div Reprod Endocrinol & Infertil Pittsburgh PA USA

Objective: The objectives of this study are to design an artificial neural network (ANN) and to test it retrospectively to determine if it may be used to predict emergency department (ED) volume. Methods: We conducted a retrospective review of patient registry data from February 4, 2007, to December 31, 2009, from an inner city, tertiary care hospital. We harvested data regarding weather, days of week, air quality, and special events to train the ANN. The ANN belongs to a class of neural networks called multilayer perceptrons. We designed an ANN composed of 37 input neurons, 22 hidden neurons, and 1 output neuron designed to predict the daily number of ED visits. The training method is a supervised backpropagation algorithm that uses mean squared error to minimize the average squared error between the ANN's output and the number of ED visits over all the example pairs. Results: A linear regression between the predicted and actual ED visits demonstrated an R-2 of 0.957 with a slope of 0.997. Ninety-five percent of the time, the ANN was within 20 visits. Conclusion: The results of this study show that a properly designed ANN is an effective tool that may be used to predict ED volume. The scatterplot demonstrates that the ANN is least predictive at the extreme ends of the spectrum suggesting that the ANN may be missing important variables. A properly calibrated ANN may have the potential to allow ED administrators to staff their units more appropriately in an effort to reduce patient wait times, decrease ED physician burnout rates, and increase the ability of caregivers to provide quality patient care. A prospective is needed to validate the utility of the ANN. (C) 2014 Elsevier Inc. All rights reserved.

关键词： emergency room Multilayer perceptrons Training methods Anniversaries and Special Events backpropagation algorithms

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An incorporate genetic algorithm based back propagation neural network model for coal and gas outburst intensity prediction

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Procedia Earth and Planetary Science 2009年第1期1卷 1285-1292页

作者： Min, Yang Wang, Yun-jia Cheng, Yuan-ping School of Environmental Science and Spatial Informatics China University of Mining and Technology Xuzhou 221116 China Jiangsu Key Laboratory for Resources and Environmental Information Engineering China University of Mining and Technology Xuzhou 221116 China School of Safety Engineering China University of Mining and Technology Xuzhou Jiangsu 221116 China

The traditional GABP model used in complex coal and gas outbursts prediction, which trains the back-propagation neural networks (BPNN) by Genetic Algorithm (GA), is provided with some limitations, such as massive time-consuming, optimal stop condition of GA pretreatment indeterminacy, independency and complex task of great importance. To overcome these problems, a new method of coal and gas outbursts intensity prediction by Incorporate Genetic Algorithm Based Back Propagation Neural Network (IGABP) is applied to determine parameters of BPNN automatically and propose an efficient GA which reduces its iterative computation time for enhancing the training capacity of BPNN. First, improved GA is based on single population model among continuous generation model and used the modified self-adapted crossover rate, crossover strategy, self-adapted stop criterion, as well as special survival condition. Second, BP operator is introduced into the evolution of GA operations, improving the standard GA optimization of random search and self-guiding optimization searching. To show the validity of the proposed method, we compare it with traditional GABP and IGABP using a dataset. The results show that the IGABP model can effectively overcome the inadequacies of the traditional model, its operating efficiency and forecast performance are improved significantly. © 2009.

关键词： Neural networks backpropagation backpropagation algorithms Coal Coal industry Computational efficiency Gas bursts Gases Genetic algorithms Locomotives Mathematical models Mathematical operators Optimization Railroad cars Back propagation neural networks coal and gas outburst Complex task Crossover rates Crossover strategies Data sets GA optimization Improved GA Improved models Intensity prediction Iterative computation time Operating efficiency Outburst intensity Population model Pre Treatment Random searches Self guiding

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Neural network-based detection of mechanical, sensor and biological faults in deep-trough hydroponics

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2003年第1-3期40卷 65-85页

作者： Ferentinos, KP Albright, LD Selman, B Cornell Univ Dept Biol & Environm Engn Ithaca NY 14853 USA Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

In this work, two separate fault detection models are developed: one for the detection of faulty operation of a deep-trough hydroponic system which is caused by mechanical, actuator or sensor faults, and one for the detection of a category of biological faults (i.e. specific stressed situations of the plants), namely the "transpiration fault". The neural network methodology was proved to be successful in the task of fault detection, in both applications. The general fault detection model was capable of detecting a faulty situation in a very short time, in most cases within 20 or 40 min. In the case of the biological fault detection model, the "transpiration fault" was generally detected within 2-3 h. The results show that both neural networks have useful generalization capabilities. The influences of the conditions of the plants to the measured root zone variables are also investigated and they show that biological faults in general cannot be detected in this kind of cultivation systems using the measurable variables used in this work. The most probable explanation is the inertia of overwhelming mass of the nutrient solution (compared to the mass of the plants), in a way that it becomes a limitation factor to the influence of plants condition to their root zone microenvironment. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： fault detection hydroponics feedforward neural networks biological faults backpropagation algorithms

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Utilization of the LMS Algorithm to Filter the Predicted Course by Means of Neural Networks for Monitoring the Occupancy of Rooms in an Intelligent Administrative Building

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IFAC-PapersOnLine 2018年第6期51卷 378-383页

作者： Vanus, J. Martinek, R. Nedoma, J. Fajkus, M. Cvejn, D. Valicek, P. Novak, T. Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava CENET VSB-TUO a RMTVC 17 Listopadu 15 Ostrava70833 Czech Republic

For monitoring the occupancy of individual rooms in an intelligent administrative building (IAB), a wide variety of sensors can be used, by means of which the presence of a person in the monitored area can be determined. For a larger administrative-type building, installation of additional sensors means considerable investment costs. As the present-day standard, temperature or also humidity sensors are installed in individual IAB rooms. The paper describes the proposed method for the determination of occupancy of the monitored area by means of prediction of the course of CO2(ppm) from the measured values of humidity rH(%), indoor temperature Ti(°C) and outdoor temperature To(°C), using the gradient algorithm of back-propagation of error for adaptation of the multilayer feedforward Artificial Neural Network (ANN) in the IAB areas with utilization of the Bayesian regularization method (BRM) to obtain information on the occupancy of individual rooms. The LMS algorithm was used to filter the predicted course in order to determine the occupancy of the monitored areas more precisely. The advantage of the proposed method is the utilization of common operating sensors to obtain information on the state of operational-technical functions in the IAB for the purpose of optimum control of the operational-technical functions of the IAB on the basis of predictable needs of persons using the IAB areas. © 2018

关键词： Adaptive filters Adaptive filtering backpropagation algorithms Least squares approximations Neural networks Space heating Artifical neural network (ANN) Bayesian regularization Dynamic time warping Intelligent Administrative Building (IAB) Least mean squares Occupancy

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Establishment of grey-neural network forecasting model of coal and gas outburst

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Procedia Earth and Planetary Science 2009年第1期1卷 148-153页

作者： Sheng-qiang, Yang Yan, Sun Zu-yun, Chen Bao-hai, Yu Quan, Xu State Key Laboratory of Mine Resource and Safe Exploitation School of Safety Engineering China University of Mining and Technology Xuzhou 221008 China Department of Public Management Shanghai Trade Union Polytechnic Shanghai 201415 China

Effect factors on coal and gas outburst are analyzed using grey correlation method so as to determine the input parameters of artificial neural network (ANN). Then using the improved BP algorithm, we choose five dominant factors of grey correlation analysis as the input parameters to establish neural network model for forecasting coal and gas outburst. This network was trained by using the learning samples collected from the instances of typical coal and gas outburst mines in China. Meanwhile, we take coal and gas outburst instances of Yunnan Enhong coal mine as forecasting samples and compare the forecasting result from these samples with that from the conventional method, indicating that this model can meet the forecasting requirements of coal and gas outburst. © 2009.

关键词： Neural networks backpropagation backpropagation algorithms Coal Coal industry Coal mines Forecasting Gas bursts Gases Mining Artificial Neural Network coal and gas outburst Conventional methods Dominant factor Effect factors Forecasting models Grey correlation Grey correlation analysis Improved BP algorithms Influencing factor Input parameter Learning samples Neural network model

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A DYNAMIC LEARNING NEURAL-NETWORK FOR REMOTE-SENSING APPLICATIONS

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1994年第5期32卷 1096-1102页

作者： TZENG, YC CHEN, KS KAO, WL FUNG, AK UNIV TEXAS WAVE SCATTERING RES CTRARLINGTONTX 76019

The neural network learning process is to adjust the network weighs to adapt the selected training data. Based on the polynomial basis function (PBF) modeled neural network that is a modified multilayer perceptrons (MLP) network, a dynamic learning algorithm (DL) is proposed in this paper. The presented learning algorithm makes use of Kalman filtering technique to update the network weights, in the sense that the stochastic characteristics of incoming data sets are implicitly incorporated into the network. The Kalman gains which represent the learning rates of the network weights updating are calculated by using the U-D factorization. By concatenating all of the network weights at each layer to form a long vector such that it can be updated without propagating back, the proposed algorithm improves the performance of convergence to which the back-propagation (BP) learning algorithm often suffers. Numerical illustrations are carried out using two categories of problems: multispectral imagery classification and surface parameters inversion. Results indicates the use of Kalman filtering algorithm not only substantially increases the convergence rate in the learning stage, but also enhances the separability for highly nonlinear boundaries problems, as compared to BP algorithm, suggesting that the proposed DL neural network provides a practical and potential tool for remote sensing applications.

关键词： Neural networks Remote sensing Filtering algorithms Kalman filters backpropagation algorithms Convergence Training data Polynomials Multi-layer neural network Multilayer perceptrons

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Application of fuzzy neural network in predicting the risk of rock burst

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Procedia Earth and Planetary Science 2009年第1期1卷 536-543页

作者： Jian, Sun Lian-guo, Wang Hua-lei, Zhang Yi-feng, Shen State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and Technology Xuzhou 221008 China School of Sciences China University of Mining and Technology Xuzhou 221116 China

Rock burst is one of the coal and rock dynamical disasters that must be kept in mind in mining activities. With the increase of mining depth, the risk of rock burst becomes increasingly great. At present, the risk prediction for rock burst mostly is still in the stage of simple statistical study and single factor forecast, making the prediction precision be not a desired one. Using the knowledge of fuzzy mathematics and neural network, we propose a fuzzy neural network risk prediction model for rock burst trained with the improved BP algorithm based on the typical rock burst data. This method is an improvement of comprehensive index judgment and multi-index judgment with fuzzy mathematics. Practical engineering applications in Sanhejian Coal Mines indicate that this method is not only precise and simple, but also intelligent, with the predicted results well agreeing with the practical conditions. Therefore, this method can be applied to the relevant engineering projects with satisfactory results. © 2009.

关键词： Risks backpropagation algorithms Coal mines Fuzzy neural networks Fuzzy sets Gas hazards Mathematical models Mining Rock bursts Rock pressure Rocks BP neural networks Engineering project Fuzzy mathematics Improved BP algorithms Mining activities Mining depth Multi index Practical engineering applications Prediction precision risk prediction Risk predictions Rock dynamical disasters Statistical study

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Face recognition based on constructive neural networks covering learning algorithm

Face recognition based on constructive neural networks cover...

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American Control Conference 2005 (ACC)

作者： Huang, GH Xiong, ZH Shao, HH Shanghai Jiao Tong Univ Inst Automat Shanghai 200030 Peoples R China

ISBN: (纸本)0780390989

A general and efficient design approach using covering neural classifier to cope with the high-dimensional and small sample size problem is proposed. For alleviating the computational burden, face features are first extracted by the principal component analysis (PCA). In order to avoid the influence of outlier classes and reduce the large overlapping of neighboring classes, a new weighted Fisher linear discriminant (WFLD) criterion is presented by weighting contributions of individual class pairs according to the Euclidian distance of the respective class means. A new learning algorithm is used to train the neural networks classifier, which uses the "sphere neighborhoods" to cover the input samples and draw up their distributions in the original space. Thus, the training problem of neural networks may be transformed into the covering problem of a point set, which avoids the iterative process, and overcomes the problem of longtime training of classical neural networks. Simulation results conducted on the ORL database show that the system achieves excellent performance both in terms of error rates of classification and learning efficiency.

关键词： Face recognition Neural networks Feature extraction Principal component analysis Shape measurement Data mining backpropagation algorithms Automation Position measurement Lighting

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Model Predictive Control Using Neural Networks

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IEEE Control Systems 1995年第5期15卷 61-66页

作者： Draeger, Andreas Engell, Sebastian Ranke, Horst Process Control Group Department of Chemical Engineering University of Dortmund D-44221 Dortmund Germany

In this article, we present the application of a neural-network-based model predictive control scheme to control pH in a laboratory-scale neutralization reactor. We use a feedforward neural network as the nonlinear prediction model in an extended DMC-algorithm to control the pH-value. The training data set for the neural network was obtained from measurements of the inputs and outputs of the real plant operating with a PI-controller. Thus, no a priori information about the dynamics of the plant and no special operating conditions of the plant were needed to design the controller. The training algorithm used is a combination of an adaptive backpropagation algorithm that tunes the connection weights with a genetic algorithm to modify the slopes of the activation function of each neuron. This combination turned out to be very robust against getting caught in local minima and it is very insensitive to the initial settings of the weights of the network. Experimental results show that the resulting control algorithm performs much better than the conventional PI-controller which was used for the generation of the training data set.< >

关键词： Predictive models Predictive control Neural networks Training data backpropagation algorithms Laboratories Inductors Feedforward neural networks Genetic algorithms Neurons

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