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Network Algorithm (network + algorithm)
Kinds of Network Algorithm Selected AbstractsEnhancing Neural Network Traffic Incident-Detection Algorithms Using WaveletsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2001A. Samant Researchers have presented freeway traffic incident-detection algorithms by combining the adaptive learning capability of neural networks with imprecision modeling capability of fuzzy logic. In this article it is shown that the performance of a fuzzy neural network algorithm can be improved through preprocessing of data using a wavelet-based feature-extraction model. In particular, the discrete wavelet transform (DWT) denoising and feature-extraction model proposed by Samant and Adeli (2000) is combined with the fuzzy neural network approach presented by Hsiao et al. (1994). It is shown that substantial improvement can be achieved using the data filtered by DWT. Use of the wavelet theory to denoise the traffic data increases the incident-detection rate, reduces the false-alarm rate and the incident-detection time, and improves the convergence of the neural network training algorithm substantially. [source] An efficient concurrent implementation of a neural network algorithmCONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 12 2006R. Andonie Abstract The focus of this study is how we can efficiently implement the neural network backpropagation algorithm on a network of computers (NOC) for concurrent execution. We assume a distributed system with heterogeneous computers and that the neural network is replicated on each computer. We propose an architecture model with efficient pattern allocation that takes into account the speed of processors and overlaps the communication with computation. The training pattern set is distributed among the heterogeneous processors with the mapping being fixed during the learning process. We provide a heuristic pattern allocation algorithm minimizing the execution time of backpropagation learning. The computations are overlapped with communications. Under the condition that each processor has to perform a task directly proportional to its speed, this allocation algorithm has polynomial-time complexity. We have implemented our model on a dedicated network of heterogeneous computers using Sejnowski's NetTalk benchmark for testing. Copyright © 2005 John Wiley & Sons, Ltd. [source] Design and development of a card-sized virtual keyboard using permanent magnets and hall sensorsELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 3 2009Kazuyuki Demachi Abstract This paper proposes a method to distinguish the key-type of human fingers attached to small permanent magnets. The Hall sensors arrayed in the credit card-size area feel the distribution of the magnetic field due to the key-typing movement of the human fingers as if a keyboard exists, and the signal is analyzed using the genetic algorithm or the neural network algorithm to distinguish the typed keys. By this method, the keyboard can be miniaturized to credit card size (54 mm × 85 mm). We called this system "the virtual keyboard system." © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(3): 32,37, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10043 [source] A new recursive neural network algorithm to forecast electricity price for PJM day-ahead marketINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 6 2010Paras Mandal Abstract This paper evaluates the usefulness of publicly available electricity market information in predicting the hourly prices in the PJM day-ahead electricity market using recursive neural network (RNN) technique, which is based on similar days (SD) approach. RNN is a multi-step approach based on one output node, which uses the previous prediction as input for the subsequent forecasts. Comparison of forecasting performance of the proposed RNN model is done with respect to SD method and other literatures. To evaluate the accuracy of the proposed RNN approach in forecasting short-term electricity prices, different criteria are used. Mean absolute percentage error, mean absolute error and forecast mean square error (FMSE) of reasonably small values were obtained for the PJM data, which has correlation coefficient of determination (R2) of 0.7758 between load and electricity price. Error variance, one of the important performance criteria, is also calculated in order to measure robustness of the proposed RNN model. The numerical results obtained through the simulation to forecast next 24 and 72,h electricity prices show that the forecasts generated by the proposed RNN model are significantly accurate and efficient, which confirm that the proposed algorithm performs well for short-term price forecasting. Copyright © 2009 John Wiley & Sons, Ltd. [source] Experimental and neural model analysis of styrene removal from polluted air in a biofilterJOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 7 2009Eldon R. Rene Abstract BACKGROUND: Biofilters are efficient systems for treating malodorous emissions. The mechanism involved during pollutant transfer and subsequent biotransformation within a biofilm is a complex process. The use of artificial neural networks to model the performance of biofilters using easily measurable state variables appears to be an effective alternative to conventional phenomenological modelling. RESULTS: An artificial neural network model was used to predict the extent of styrene removal in a perlite-biofilter inoculated with a mixed microbial culture. After a 43 day biofilter acclimation period, styrene removal experiments were carried out by subjecting the bioreactor to different flow rates (0.15,0.9 m3 h,1) and concentrations (0.5,17.2 g m,3), that correspond to inlet loading rates up to 1390 g m,3 h,1. During the different phases of continuous biofilter operation, greater than 92% styrene removal was achievable for loading rates up to 250 g m,3 h,1. A back propagation neural network algorithm was applied to model and predict the removal efficiency (%) of this process using inlet concentration (g m,3) and unit flow (h,1) as input variables. The data points were divided into training (115 × 3) and testing set (42 × 3). The most reliable condition for the network was selected by a trial and error approach and by estimating the determination coefficient (R2) value (0.98) achieved during prediction of the testing set. CONCLUSION: The results showed that a simple neural network based model with a topology of 2,4,1 was able to efficiently predict the styrene removal performance in the biofilter. Through sensitivity analysis, the most influential input parameter affecting styrene removal was ascertained to be the flow rate. Copyright © 2009 Society of Chemical Industry [source] A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR studyJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 10 2010Jingheng Wu Abstract Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q2) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010 [source] Developing a new collection-evaluation method: Mapping and the user-side h-indexJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 11 2009Pan Jun Kim This study proposes a new visualization method and index for collection evaluation. Specifically, it develops a network-based mapping technique and a user-focused Hirsch index (user-side h-index) given the lack of previous studies on collection evaluation methods that have used the h-index. A user-side h-index is developed and compared with previous indices (use factor, difference of percentages, collection-side h-index) that represent the strengths of the subject classes of a library collection. The mapping procedure includes the subject-usage profiling of 63 subject classes and collection-usage map generations through the pathfinder network algorithm. Cluster analyses are then conducted upon the pathfinder network to generate 5 large and 14 small clusters. The nodes represent the strengths of the subject-class usages reflected by the user-side h-index. The user-side h-index was found to have advantages (e.g., better demonstrating the real utility of each subject class) over the other indices. It also can more clearly distinguish the strengths between the subject classes than can collection-side h-index. These results may help to identify actual usage and strengths of subject classes in library collections through visualized maps. This may be a useful rationale for the establishment of the collection-development plan. [source] Ridge directional singular points for fingerprint recognition and matchingAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 1 2006Issam Dagher Abstract In this paper, a new approach to extract singular points in a fingerprint image is presented. It is usually difficult to locate the exact position of a core or a delta due to the noisy nature of fingerprint images. These points are the most widely used for fingerprint classification and matching. Image enhancement, thinning, cropping, and alignment are used for minutiae extraction. Based on the Poincaré curve obtained from the directional image, our algorithm extracts the singular points in a fingerprint with high accuracy. It examines ridge directions when singular points are missing. The algorithm has been tested for classification performance on the NIST-4 fingerprint database and found to give better results than the neural networks algorithm. Copyright © 2005 John Wiley & Sons, Ltd. [source] | ||||||||||||||||