Journal articles: 'Output error'

1

TOMITA, YUTAKA, AD A. H. DAMEN, and PAUL M. J. VAN DEN HOF. "Equation error versus output error methods." Ergonomics 35, no. 5-6 (May 1992): 551–64. http://dx.doi.org/10.1080/00140139208967836.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Popović, Maja, and Hermann Ney. "Towards Automatic Error Analysis of Machine Translation Output." Computational Linguistics 37, no. 4 (December 2011): 657–88. http://dx.doi.org/10.1162/coli_a_00072.

Full text

Abstract:

Evaluation and error analysis of machine translation output are important but difficult tasks. In this article, we propose a framework for automatic error analysis and classification based on the identification of actual erroneous words using the algorithms for computation of Word Error Rate (WER) and Position-independent word Error Rate (PER), which is just a very first step towards development of automatic evaluation measures that provide more specific information of certain translation problems. The proposed approach enables the use of various types of linguistic knowledge in order to classify translation errors in many different ways. This work focuses on one possible set-up, namely, on five error categories: inflectional errors, errors due to wrong word order, missing words, extra words, and incorrect lexical choices. For each of the categories, we analyze the contribution of various POS classes. We compared the results of automatic error analysis with the results of human error analysis in order to investigate two possible applications: estimating the contribution of each error type in a given translation output in order to identify the main sources of errors for a given translation system, and comparing different translation outputs using the introduced error categories in order to obtain more information about advantages and disadvantages of different systems and possibilites for improvements, as well as about advantages and disadvantages of applied methods for improvements. We used Arabic–English Newswire and Broadcast News and Chinese–English Newswire outputs created in the framework of the GALE project, several Spanish and English European Parliament outputs generated during the TC-Star project, and three German–English outputs generated in the framework of the fourth Machine Translation Workshop. We show that our results correlate very well with the results of a human error analysis, and that all our metrics except the extra words reflect well the differences between different versions of the same translation system as well as the differences between different translation systems.

APA, Harvard, Vancouver, ISO, and other styles

3

Cui, Pengcheng, Bin Li, Jing Tang, Jiangtao Chen, and Youqi Deng. "A modified adjoint-based grid adaptation and error correction method for unstructured grid." Modern Physics Letters B 32, no. 12n13 (May 10, 2018): 1840020. http://dx.doi.org/10.1142/s0217984918400201.

Full text

Abstract:

Grid adaptation is an important strategy to improve the accuracy of output functions (e.g. drag, lift, etc.) in computational fluid dynamics (CFD) analysis and design applications. This paper presents a modified robust grid adaptation and error correction method for reducing simulation errors in integral outputs. The procedure is based on discrete adjoint optimization theory in which the estimated global error of output functions can be directly related to the local residual error. According to this relationship, local residual error contribution can be used as an indicator in a grid adaptation strategy designed to generate refined grids for accurately estimating the output functions. This grid adaptation and error correction method is applied to subsonic and supersonic simulations around three-dimensional configurations. Numerical results demonstrate that the sensitive grids to output functions are detected and refined after grid adaptation, and the accuracy of output functions is obviously improved after error correction. The proposed grid adaptation and error correction method is shown to compare very favorably in terms of output accuracy and computational efficiency relative to the traditional featured-based grid adaptation.

APA, Harvard, Vancouver, ISO, and other styles

4

HAMIDI, HODJAT, ABBAS VAFAEI, and SEYED AMIRHASSAN MONADJEMI. "ANALYSIS AND DESIGN OF AN ABFT AND PARITY-CHECKING TECHNIQUE IN HIGH PERFORMANCE COMPUTING SYSTEMS." Journal of Circuits, Systems and Computers 21, no. 03 (May 2012): 1250017. http://dx.doi.org/10.1142/s021812661250017x.

Full text

Abstract:

We present a new approach to algorithm-based fault tolerance (ABFT) and parity-checking techniques in the design of high performance computing systems. The ABFT technique employs real convolution error-correcting codes to encode the input data. In order to reduce the round-off error from the output decoding process, systematic real convolution encoding is employed. This paper proposes an efficient method to detect the arithmetic errors using convolution codes at the output compared with an equivalent parity value derived from the input data. Number data processing errors are detected by comparing parity values associated with a convolution code. These comparable sets will be very close numerically, although not identical because of round-off error differences between the two parity generation processes. The effects of internal failures and round-off error are modeled by additive error sources located at the output of the processing block and input at threshold detector. This model combines the aggregate effects of errors and applies them to the respective outputs.

APA, Harvard, Vancouver, ISO, and other styles

5

Windridge, David, Riccardo Mengoni, and Rajagopal Nagarajan. "Quantum error-correcting output codes." International Journal of Quantum Information 16, no. 08 (December 2018): 1840003. http://dx.doi.org/10.1142/s0219749918400038.

Full text

Abstract:

Quantum machine learning is the aspect of quantum computing concerned with the design of algorithms capable of generalized learning from labeled training data by effectively exploiting quantum effects. Error-correcting output codes (ECOC) are a standard setting in machine learning for efficiently rendering the collective outputs of a binary classifier, such as the support vector machine, as a multi-class decision procedure. Appropriate choice of error-correcting codes further enables incorrect individual classification decisions to be effectively corrected in the composite output. In this paper, we propose an appropriate quantization of the ECOC process, based on the quantum support vector machine. We will show that, in addition to the usual benefits of quantizing machine learning, this technique leads to an exponential reduction in the number of logic gates required for effective correction of classification error.

APA, Harvard, Vancouver, ISO, and other styles

6

Escalera, Sergio, David Masip, Eloi Puertas, Petia Radeva, and Oriol Pujol. "Online error correcting output codes." Pattern Recognition Letters 32, no. 3 (February 2011): 458–67. http://dx.doi.org/10.1016/j.patrec.2010.11.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Rosenqvist, Fredrik, and Anders Karlström. "Piecewise-Linear Output-Error Models." IFAC Proceedings Volumes 36, no. 16 (September 2003): 1795–800. http://dx.doi.org/10.1016/s1474-6670(17)35020-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Sasson, Ariella, and Todd P. Michael. "Filtering error from SOLiD Output." Bioinformatics 26, no. 6 (March 15, 2010): 849–50. http://dx.doi.org/10.1093/bioinformatics/btq045.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Poladi, Irfan, and Hitesh Ishwardas. "Review paper on Error Correcting Output Code Based on Multiclass Classification." International Journal of Scientific Research 2, no. 2 (June 1, 2012): 134–36. http://dx.doi.org/10.15373/22778179/feb2013/45.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Gopalratnam, G., and J. R. Raol. "Analysis of stabilised output error methods." IEE Proceedings - Control Theory and Applications 143, no. 2 (March 1, 1996): 209–17. http://dx.doi.org/10.1049/ip-cta:19960195.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Ma, Chao, Ivor W. Tsang, Fumin Shen, and Chuancai Liu. "Error Correcting Input and Output Hashing." IEEE Transactions on Cybernetics 49, no. 3 (March 2019): 781–91. http://dx.doi.org/10.1109/tcyb.2017.2785621.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Mayyas, K., and T. Aboulnasr. "On transient error surfaces of output error IIR adaptive filtering." IEEE Transactions on Signal Processing 46, no. 3 (March 1998): 766–71. http://dx.doi.org/10.1109/78.661343.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

PETRELLIS, N., G. ADAM, and D. VENTZAS. "MONOTONIC ERROR ELIMINATION IN SUBRANGE A/D CONVERTERS." Journal of Circuits, Systems and Computers 22, no. 01 (January 2013): 1250073. http://dx.doi.org/10.1142/s0218126612500739.

Full text

Abstract:

Monotonic errors cause severe errors and are inherent in several A/D Converter (ADC) architectures. Moreover, several error correcting and ADC output processing methods require a monotonic behavior for a successful operation. Based on the features of asynchronous ADCs, an architecture for the elimination of monotonic errors is presented. This monotonic error correcting module is connected at the output of an ADC and does not require any modification in its internal circuits. It controls an output buffering stage that discards output codes with monotonic errors and this correcting procedure is triggered by changes in specific output bits of the ADC. Simulation results show an improvement by 8 dB or 25% maximum, in the signal-to-noise and distortion ratio (SNDR) of an 8-bit ADC if this monotonic error elimination method is used alone and a further improvement by 1–5 dB if it is combined with a post processing method developed by the authors. Similar improvement can also be achieved in several other architectures like Subrange or Folding ADCs that operate in relatively high oversampling ratio and suffer from monotonic errors with specific features.

APA, Harvard, Vancouver, ISO, and other styles

14

Beven, K. "On the concept of model structural error." Water Science and Technology 52, no. 6 (September 1, 2005): 167–75. http://dx.doi.org/10.2166/wst.2005.0165.

Full text

Abstract:

A consideration of model structural error leads to some particularly interesting tensions in the model calibration/conditioning process. In applying models we can usually only assess the total error on some output variable for which we have observations. This total error may arise due to input and boundary condition errors, model structural errors and error on the output observation itself (not only measurement error but also as a result of differences in meaning between what is modelled and what is measured). Statistical approaches to model uncertainty generally assume that the errors can be treated as an additive term on the (possibly transformed) model output. This allows for compensation of all the sources of error, as if the model predictions are correct and the total error can be treated as “measurement error.” Model structural error is not easily evaluated within this framework. An alternative approach to put more emphasis on model evaluation and rejection is suggested. It is recognised that model success or failure within this framework will depend heavily on an assessment of both input data errors (the “perfect” model will not produce acceptable results if driven with poor input data) and effective observation error (including a consideration of the meaning of observed variables relative to those predicted by a model).

APA, Harvard, Vancouver, ISO, and other styles

15

Jeong, Gu-Min, Kyoungwoo Lee, Sang-Il Choi, Sang-Hoon Ji, and Nikil Dutt. "Effect of Soft Errors in Iterative Learning Control and Compensation using Cross-layer Approach." International Journal of Computers Communications & Control 14, no. 3 (May 31, 2019): 359–74. http://dx.doi.org/10.15837/ijccc.2019.3.3513.

Full text

Abstract:

In this paper, we study the effects of radiation-induced soft errors in iterative learning control(ILC) and present the compensation techniques to make the ILC systems robust against soft errors. Soft errors are transient faults, which occur temporarily in memories where the energetic particles strike the sensitive region in the transistors mainly under abnormal conditions such as high radiation, high temperature, and high pressure. These soft errors can cause bit value changes without any notification to the controller, affect the stability of the system, and result in catastrophic consequences. First, we investigate and analyze the effects of soft errors in the ILC systems. Our analytical study shows that when a single soft error occurs in the output data from the ILC, the performance of the learning control is significantly degraded. Second, we propose novel learning methods by incorporating the existing techniques across the system abstraction levels in the ILC to compensate for soft-error-induced incorrect output. The occurrence of soft errors is estimated by using a monotonic convergence of the erroneous outputs in a cross-layer manner, and our proposed methods can significantly reduce these negative impacts on the system performance. Under the assumption of soft error occurrence, our analytic study has proved the convergence of the proposed methods in the ILC systems and our simulation results show the effectiveness of the proposed methods to efficiently reduce the impacts of soft-error-induced outputs in the ILC systems.

APA, Harvard, Vancouver, ISO, and other styles

16

Li, Jiao, and Jun Zhao. "Output regulation for switched discrete-time linear systems via error feedback: an output error-dependent switching method." IET Control Theory & Applications 8, no. 10 (July 3, 2014): 847–54. http://dx.doi.org/10.1049/iet-cta.2013.0797.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Wigren, T. "Output error convergence of adaptive filters with compensation for output nonlinearities." IEEE Transactions on Automatic Control 43, no. 7 (July 1998): 975–78. http://dx.doi.org/10.1109/9.701104.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Taylor, Andrew A., and Lance M. Leslie. "A Single-Station Approach to Model Output Statistics Temperature Forecast Error Assessment." Weather and Forecasting 20, no. 6 (December 1, 2005): 1006–20. http://dx.doi.org/10.1175/waf893.1.

Full text

Abstract:

Abstract Error characteristics of model output statistics (MOS) temperature forecasts are calculated for over 200 locations around the continental United States. The forecasts are verified on a station-by-station basis for the year 2001. Error measures used include mean algebraic error (bias), mean absolute error (MAE), relative frequency of occurrence of bias and MAE values, and the daily forecast errors themselves. A case study examining the spatial and temporal evolution of MOS errors is also presented. The error characteristics presented here, together with the case study, provide a more detailed evaluation of MOS performance than may be obtained from regionally averaged error statistics. Knowledge concerning locations where MOS forecasts have large errors or biases and why those errors or biases exist is of great value to operational forecasters. Not only does such knowledge help improve their forecasts, but forecaster performance is often compared to MOS predictions. Examples of biases in MOS forecast errors are illustrated by examining two stations in detail. Significant warm and cold biases are found in maximum temperature forecasts for Los Angeles, California (LAX), and minimum temperature forecasts for Las Vegas, Nevada (LAS), respectively. MAE values for MOS temperature predictions calculated in this study suggest that coastal stations tend to have lower MAE values and lower variability in their errors, while forecasts with high MAE and error variability are more frequent in the interior of the United States. Therefore, MAE values from samples of MOS forecasts are directly proportional to the variance in the observations. Additionally, it is found that daily maximum temperature forecast errors exhibit less variability during the summer months than they do over the rest of the year, and that forecasts for any one station rarely follow a consistent temporal pattern for more than two or three consecutive days. These inconsistent error patterns indicate that forecasting temperatures based on recent trends in MOS forecast errors at an individual station is usually not a good strategy. As shown in earlier studies by other authors and demonstrated again here, MOS temperature forecasts are often inaccurate in the vicinity of strong temperature gradients, for locations affected by shallow cold air masses, or for stations in regions of anomalously warm or cold temperatures. Finally, a case study is presented examining the spatial and temporal distributions of MOS temperature forecast errors across the United States from 13 to 15 February 2001. During this period, two surges of cold arctic air moved south into the United States. In contrast to error trends at individual stations, nationwide spatial and temporal patterns of MOS forecast errors could prove to be a powerful forecasting tool. Nationwide plots of errors in MOS forecasts would be useful if made available in real time to operational forecasters.

APA, Harvard, Vancouver, ISO, and other styles

19

Busaba, Fadi, and Parag K. Lala. "Techniques for Self-Checking Combinational Logic Synthesis." VLSI Design 2, no. 3 (January 1, 1994): 209–21. http://dx.doi.org/10.1155/1994/29238.

Full text

Abstract:

This paper presents techniques for designing arbitrary combinational circuits so that any single stuck-at fault will result in either single bit error or unidirectional multibit error at the output. If the outputs are encoded using Berger code or m-out-of-n code, then the proposed technique will enable on-line detection of faults in the circuit. An algorithm for indicating whether a certain fault at an input will create bidirectional error at the output is presented. An input encoding algorithm and an output encoding algorithm that ensure that every fault will either produce single bit error or unidirectional multibit error at the output are proposed. If there are no input fault which produces bidirectional error, no internal stuck-at fault will result in such an error irrespective of the way the circuit is implemented. Thus, only single bit or unidirectional multibit error will result in the presence of a fault in the circuit. The proposed techniques have been applied to MCNC benchmark circuits and the overhead is estimated.

APA, Harvard, Vancouver, ISO, and other styles

20

Park, Hyunbin, and Shiho Kim. "Single Inductor Multiple Output Auto-Buck-Boost DC–DC Converter with Error-Driven Randomized Control." Electronics 9, no. 9 (August 19, 2020): 1335. http://dx.doi.org/10.3390/electronics9091335.

Full text

Abstract:

We propose a single inductor multiple output (SIMO) auto-buck-boost DC–DC converter with error-driven randomized control (EDRC). The conventional controls in a SIMO DC–DC converter supply power to outputs that have been selected in a sequential order. Furthermore, they control the inductor current levels at either edge of a switching period in a steady state to be at the same level to alleviate cross-regulation. However, this limits the flexibility of the converter to respond to changes in load requirements. A sequential selection of light loads results in these loads being selected more often than a load demand, degrading the efficiency for light loads. In addition, limited flexibility leads to delayed responses. This paper introduces an auto-buck-boost topology that selects outputs based on output errors, and instantaneously adjusts the inductor current level. Moreover, we propose a technique for allowing any output to avoid selection when all outputs are fully supplied. The proposed EDRC scheme achieves improvements in efficiency in regards to light loads, cross-regulation, and output driving capability.

APA, Harvard, Vancouver, ISO, and other styles

21

Michaeli, Linus, and Ján Šaliga. "Error Models of the Analog to Digital Converters." Measurement Science Review 14, no. 2 (April 1, 2014): 62–77. http://dx.doi.org/10.2478/msr-2014-0010.

Full text

Abstract:

Abstract Error models of the Analog to Digital Converters describe metrological properties of the signal conversion from analog to digital domain in a concise form using few dominant error parameters. Knowledge of the error models allows the end user to provide fast testing in the crucial points of the full input signal range and to use identified error models for post correction in the digital domain. The imperfections of the internal ADC structure determine the error characteristics represented by the nonlinearities as a function of the output code. Progress in the microelectronics and missing information about circuital details together with the lack of knowledge about interfering effects caused by ADC installation prefers another modeling approach based on the input-output behavioral characterization by the input-output error box. Internal links in the ADC structure cause that the input-output error function could be described in a concise form by suitable function. Modeled functional parameters allow determining the integral error parameters of ADC. Paper is a survey of error models starting from the structural models for the most common architectures and their linkage with the behavioral models represented by the simple look up table or the functional description of nonlinear errors for the output codes.

APA, Harvard, Vancouver, ISO, and other styles

22

EISAKA, Toshio, and Ryozaburo TAGAWA. "DDC Algorithm with Small Quantization Output Error." Transactions of the Society of Instrument and Control Engineers 23, no. 7 (1987): 692–98. http://dx.doi.org/10.9746/sicetr1965.23.692.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Bautista, Miguel Ángel, Sergio Escalera, Xavier Baró, Petia Radeva, Jordi Vitriá, and Oriol Pujol. "Minimal design of error-correcting output codes." Pattern Recognition Letters 33, no. 6 (April 2012): 693–702. http://dx.doi.org/10.1016/j.patrec.2011.09.023.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Vogt, M. A., L. Wozniak, and T. R. Whittemore. "Output error identification of hydrogenerator conduit dynamics." IEEE Transactions on Energy Conversion 4, no. 3 (1989): 329–36. http://dx.doi.org/10.1109/60.43232.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Gu, Shilin, Yang Cai, Jincheng Shan, and Chenping Hou. "Active learning with error-correcting output codes." Neurocomputing 364 (October 2019): 182–91. http://dx.doi.org/10.1016/j.neucom.2019.06.064.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Vogt, M. A., L. Wozniak, and T. R. Whittemore. "Output Error Identification of Hydrogenerator Conduit Dynamics." IEEE Power Engineering Review 9, no. 9 (1989): 34–35. http://dx.doi.org/10.1109/mper.1989.4310946.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Góes, Luiz Carlos Sandoval, Elder Moreira Hemerly, Benedito Carlos de Oliveira Maciel, Wilson Rios Neto, CelsoBraga Mendonca, and João Hoff. "Aircraft parameter estimation using output-error methods." Inverse Problems in Science and Engineering 14, no. 6 (September 2006): 651–64. http://dx.doi.org/10.1080/17415970600573544.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Murray, A. T. "Minimizing Aggregation Error in Input-Output Models." Environment and Planning A: Economy and Space 30, no. 6 (June 1998): 1125–28. http://dx.doi.org/10.1068/a301125.

Full text

Abstract:

The aggregation of industry sectors in input-output modeling is a common step in a regional analysis process. The author develops an optimization-based approach for identifying aggregation schemes which minimize the resulting error or information loss. Example problems previously reported in the literature are utilized for demonstrating the effectiveness of this approach and point out the shortcomings of previously reported results.

APA, Harvard, Vancouver, ISO, and other styles

29

Yamaguchi, Nobuhiko, and Naohiro Ishii. "Combining classifiers in error correcting output coding." Systems and Computers in Japan 35, no. 4 (2004): 9–18. http://dx.doi.org/10.1002/scj.10549.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Lenzen, Manfred. "Aggregating input–output systems with minimum error." Economic Systems Research 31, no. 4 (May 28, 2019): 594–616. http://dx.doi.org/10.1080/09535314.2019.1609911.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Kittler, J., R. Ghaderi, T. Windeatt, and J. Matas. "Face verification via error correcting output codes." Image and Vision Computing 21, no. 13-14 (December 2003): 1163–69. http://dx.doi.org/10.1016/j.imavis.2003.09.013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Stojanovic, Vladimir, and Vojislav Filipovic. "Adaptive Input Design for Identification of Output Error Model with Constrained Output." Circuits, Systems, and Signal Processing 33, no. 1 (July 17, 2013): 97–113. http://dx.doi.org/10.1007/s00034-013-9633-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Amairi, Messaoud. "Recursive set membership estimation for output–error fractional models with unknown–but–bounded errors." International Journal of Applied Mathematics and Computer Science 26, no. 3 (September 1, 2016): 543–53. http://dx.doi.org/10.1515/amcs-2016-0038.

Full text

Abstract:

Abstract This paper presents a new formulation for set-membership parameter estimation of fractional systems. In such a context, the error between the measured data and the output model is supposed to be unknown but bounded with a priori known bounds. The bounded error is specified over measurement noise, rather than over an equation error, which is mainly motivated by experimental considerations. The proposed approach is based on the optimal bounding ellipsoid algorithm for linear output-error fractional models. A numerical example is presented to show effectiveness and discuss results.

APA, Harvard, Vancouver, ISO, and other styles

34

Akbari, Mahmood, and Abbas Afshar. "Similarity-based error prediction approach for real-time inflow forecasting." Hydrology Research 45, no. 4-5 (November 5, 2013): 589–602. http://dx.doi.org/10.2166/nh.2013.098.

Full text

Abstract:

Regardless of extensive researches on hydrologic forecasting models, the issue of updating the outputs from forecasting models has remained a main challenge. Most of the existing output updating methods are mainly based on the presence of persistence in the errors. This paper presents an alternative approach to updating the outputs from forecasting models in order to produce more accurate forecast results. The approach uses the concept of the similarity in errors for error prediction. The K nearest neighbor (KNN) algorithm is employed as a similarity-based error prediction model and improvements are made by new data, and two other forms of the KNN are developed in this study. The KNN models are applied for the error prediction of flow forecasting models in two catchments and the updated flows are compared to those of persistence-based methods such as autoregressive (AR) and artificial neural network (ANN) models. The results show that the similarity-based error prediction models can be recognized as an efficient alternative for real-time inflow forecasting, especially where the persistence in the error series of flow forecasting model is relatively low.

APA, Harvard, Vancouver, ISO, and other styles

35

Porr, Bernd, and Paul Miller. "Forward propagation closed loop learning." Adaptive Behavior 28, no. 3 (May 31, 2019): 181–94. http://dx.doi.org/10.1177/1059712319851070.

Full text

Abstract:

For an autonomous agent, the inputs are the sensory data that inform the agent of the state of the world, and the outputs are their actions, which act on the world and consequently produce new sensory inputs. The agent only knows of its own actions via their effect on future inputs; therefore desired states, and error signals, are most naturally defined in terms of the inputs. Most machine learning algorithms, however, operate in terms of desired outputs. For example, backpropagation takes target output values and propagates the corresponding error backwards through the network in order to change the weights. In closed loop settings, it is far more obvious how to define desired sensory inputs than desired actions, however. To train a deep network using errors defined in the input space would call for an algorithm that can propagate those errors forwards through the network, from input layer to output layer, in much the same way that activations are propagated. In this article, we present a novel learning algorithm which performs such ‘forward-propagation’ of errors. We demonstrate its performance, first in a simple line follower and then in a 1st person shooter game.

APA, Harvard, Vancouver, ISO, and other styles

36

Wang, XiaoQiong, Atilla Incecik, Zhixiong Li, Bin Hu, and Yong Ma. "Transient uniformity model predictive control in dealing with non-uniformity of multivariable systems." Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 234, no. 1 (September 9, 2019): 3–14. http://dx.doi.org/10.1177/1475090219872087.

Full text

Abstract:

In this article, the model predictive control scheme is studied for a class of multiple-input multiple-output linear systems with different transient trajectories among the outputs. To eliminate the transient errors among the outputs, a modified model predictive control scheme is designed making one output track following the reference while the other outputs track following this output instead of the reference. Utilizing the specified output as the alternative reference for all the other outputs, a constructive model predictive control scheme is developed to diminish the differences of the transient-state trajectories among each output such that both the uniformity of the transient-state trajectories and the optimal steady-state trajectories are achieved. The effectiveness of the proposed transient uniformity model predictive control scheme is demonstrated in the experiment of the semiconductor wafer manufacturing baking process. The experimental results show that the transient uniformity model predictive control scheme has successfully reduced the integral square error of transient-state uniformity between any two outputs by 90% as compared to the conventional model predictive control scheme. The robustness of the proposed scheme has also been experimentally evaluated in the presence of external disturbance and plant modeling inaccuracy.

APA, Harvard, Vancouver, ISO, and other styles

37

Abolude, Akintayo, and Wen Zhou. "Assessment and Performance Evaluation of a Wind Turbine Power Output." Energies 11, no. 8 (August 1, 2018): 1992. http://dx.doi.org/10.3390/en11081992.

Full text

Abstract:

Estimation errors have constantly been a technology bother for wind power management, often time with deviations of actual power curve (APC) from the turbine power curve (TPC). Power output dispersion for an operational 800 kW turbine was analyzed using three averaging tine steps of 1-min, 5-min, and 15-min. The error between the APC and TPC in kWh was about 25% on average, irrespective of the time of the day, although higher magnitudes of error were observed during low wind speeds and poor wind conditions. The 15-min averaged time series proved more suitable for grid management and energy load scheduling, but the error margin was still a major concern. An effective power curve (EPC) based on the polynomial parametric wind turbine power curve modeling technique was calibrated using turbine and site-specific performance data. The EPC reduced estimation error to about 3% in the aforementioned time series during very good wind conditions. By integrating statistical wind speed forecasting methods and site-specific EPCs, wind power forecasting and management can be significantly improved without compromising grid stability.

APA, Harvard, Vancouver, ISO, and other styles

38

Zivic, Natasa. "Principles of Soft Verification." International Journal of Distributed Systems and Technologies 4, no. 1 (January 2013): 1–15. http://dx.doi.org/10.4018/jdst.2013010101.

Full text

Abstract:

This paper considers messages protected with the Message Authentication Code (MAC) for the sake of authenticity. The standard forward error correcting channel code is assumed, which reduces the error rate, but no repeat mechanism exists to correct the remaining errors. The uncorrected errors cause the rejection of messages with a wrong MAC, as a successful MAC verification (“hard” verification) demands errorless message and errorless MAC. This paper introduces the extension of “hard” verification of MACs, whose result is “true” or “false”, to “soft” verification, that outputs additionally a trust level as verification result. This allows the acceptance of corrected messages and their MACs, even if a few bits of the MAC are different from the expected value. The costs are a loss of trust, as trust is defined for the successful standard or “hard” verification, i.e. for errorless message and its MAC. Therefore “Trust Output” is accompanied with the output of the verification process. A definition of “Trust Level” will be given, together with an algorithm of “soft” verification, which provides such Trust Output. This algorithm is based on a Soft Output channel decoder, which provides a reliability value for each bit, which is used as soft input for the proposed algorithm, “Soft Input Trust Output”. Simulation results show an essential improvement of the acceptance rate of MACs - at the cost of a reduced trust level. The reduction can be calculated and the maximum permitted reduction of the trust level can be preset.

APA, Harvard, Vancouver, ISO, and other styles

39

Kajdanowicz, Tomasz, and Przemysław Kazienko. "Multi-label classification using error correcting output codes." International Journal of Applied Mathematics and Computer Science 22, no. 4 (December 28, 2012): 829–40. http://dx.doi.org/10.2478/v10006-012-0061-2.

Full text

Abstract:

A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification problem. The experimental results revealed that (i) the Bode-Chaudhuri-Hocquenghem (BCH) code matched with any multi-label classifier results in better classification quality; (ii) the accuracy of the binary relevance classification method strongly depends on the coding scheme; (iii) the label power-set and the RAkEL classifier consume the same time for computation irrespective of the coding utilized; (iv) in general, they are not suitable for ECOCs because they are not capable to benefit from ECOC correcting abilities; (v) the all-pairs code combined with binary relevance is not suitable for datasets with larger label sets.

APA, Harvard, Vancouver, ISO, and other styles

40

YEO, Inho, Sooboong SHIN, Hea Sung LEE, and Sung-Pil CHANG. "Structural Damage Assessment by Regularized Output Error Estimator." IABSE Congress Report 16, no. 9 (January 1, 2000): 1154–61. http://dx.doi.org/10.2749/222137900796313843.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Zhong, Guoqiang, and Cheng-Lin Liu. "Error-correcting output codes based ensemble feature extraction." Pattern Recognition 46, no. 4 (April 2013): 1091–100. http://dx.doi.org/10.1016/j.patcog.2012.10.015.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Maddi, Abdelkader, Abderrezak Guessoum, and Daoud Berkani. "Improvement Instrumental Variables Method for Output Error Model." Procedia Computer Science 158 (2019): 84–90. http://dx.doi.org/10.1016/j.procs.2019.09.030.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Lachaize, Marie, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Aude Maitrot, and Roger Reynaud. "Evidential framework for Error Correcting Output Code classification." Engineering Applications of Artificial Intelligence 73 (August 2018): 10–21. http://dx.doi.org/10.1016/j.engappai.2018.04.019.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Dong, Shijian, Tao Liu, and Fengwei Chen. "Output Error Model Identification Against Unexpected Load Disturbance." IFAC-PapersOnLine 49, no. 7 (2016): 863–68. http://dx.doi.org/10.1016/j.ifacol.2016.07.298.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Bagheri, Mohammad Ali, Gholam Ali Montazer, and Ehsanollah Kabir. "A subspace approach to error correcting output codes." Pattern Recognition Letters 34, no. 2 (January 2013): 176–84. http://dx.doi.org/10.1016/j.patrec.2012.09.010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Jalloul, Louay M. A., Sam P. Alex, and Mohammad M. Mansour. "Soft-Output MIMO Detectors with Channel Estimation Error." IEEE Signal Processing Letters 22, no. 7 (July 2015): 993–97. http://dx.doi.org/10.1109/lsp.2014.2374425.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Varanasi, Santhosh Kumar, Chaitanya Manchikatla, and Phanindra Jampana. "Input Design for Continuous Time Output Error Models." Industrial & Engineering Chemistry Research 58, no. 26 (March 2019): 11175–86. http://dx.doi.org/10.1021/acs.iecr.8b05036.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Ninness, Brett, Håkan Hjalmarsson, and Fredrik Gustafsson. "Asymptotic variance expressions for output error model structures." IFAC Proceedings Volumes 32, no. 2 (July 1999): 4135–40. http://dx.doi.org/10.1016/s1474-6670(17)56705-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Xue, Aijun, and Xiaodan Wang. "Cost-sensitive design of error correcting output codes." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 10 (May 12, 2017): 1871–81. http://dx.doi.org/10.1177/0954406217709303.

Full text

Abstract:

Many real world applications involve multiclass cost-sensitive learning problems. However, some well-worked binary cost-sensitive learning algorithms cannot be extended into multiclass cost-sensitive learning directly. It is meaningful to decompose the complex multiclass cost-sensitive classification problem into a series of binary cost-sensitive classification problems. So, in this paper we propose an alternative and efficient decomposition framework, using the original error correcting output codes. The main problem in our framework is how to evaluate the binary costs for each binary cost-sensitive base classifier. To solve this problem, we proposed to compute the expected misclassification costs starting from the given multiclass cost matrix. Furthermore, the general formulations to compute the binary costs are given. Experimental results on several synthetic and UCI datasets show that our method can obtain comparable performance in comparison with the state-of-the-art methods.

APA, Harvard, Vancouver, ISO, and other styles

50

Oya, Masahiro, Toshihiro Kobayashi, and Shinichi Sagara. "Model Reference Adaptive Control with Output Error Feedback." IFAC Proceedings Volumes 30, no. 11 (July 1997): 335–40. http://dx.doi.org/10.1016/s1474-6670(17)42869-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Output error'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles