Log in

Relevant bibliographies by topics / Back propagation (Artificial intelligence) Neural networks (Computer science)

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers

Academic literature on the topic 'Back propagation (Artificial intelligence) Neural networks (Computer science)'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Back propagation (Artificial intelligence) Neural networks (Computer science).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Back propagation (Artificial intelligence) Neural networks (Computer science)"

1

Thatoi, Dhirendranath, Punyaslok Guru, Prabir Kumar Jena, Sasanka Choudhury, and Harish Chandra Das. "Comparison of CFBP, FFBP, and RBF Networks in the Field of Crack Detection." Modelling and Simulation in Engineering 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/292175.

Full text

Abstract:

The issue of crack detection and its diagnosis has gained a wide spread of industrial interest. The crack/damage affects the industrial economic growth. So early crack detection is an important aspect in the point of view of any industrial growth. In this paper a design tool ANSYS is used to monitor various changes in vibrational characteristics of thin transverse cracks on a cantilever beam for detecting the crack position and depth and was compared using artificial intelligence techniques. The usage of neural networks is the key point of development in this paper. The three neural networks used are cascade forward back propagation (CFBP) network, feed forward back propagation (FFBP) network, and radial basis function (RBF) network. In the first phase of this paper theoretical analysis has been made and then the finite element analysis has been carried out using commercial software, ANSYS. In the second phase of this paper the neural networks are trained using the values obtained from a simulated model of the actual cantilever beam using ANSYS. At the last phase a comparative study has been made between the data obtained from neural network technique and finite element analysis.

APA, Harvard, Vancouver, ISO, and other styles

2

Horikawa, S. i., T. Furuhashi, and Y. Uchikawa. "On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm." IEEE Transactions on Neural Networks 3, no. 5 (1992): 801–6. http://dx.doi.org/10.1109/72.159069.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Frye, R. C., E. A. Rietman, and C. C. Wong. "Back-propagation learning and nonidealities in analog neural network hardware." IEEE Transactions on Neural Networks 2, no. 1 (1991): 110–17. http://dx.doi.org/10.1109/72.80296.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Papa, F. J., R. C. Stone, and D. G. Aldrich. "A Neural Network-Based Differential Diagnosis Assessment Instrument." Journal of Educational Computing Research 10, no. 3 (1994): 277–90. http://dx.doi.org/10.2190/v5dj-u929-5rf9-je98.

Full text

Abstract:

Medical educators have been unable to produce convincing evidence of the construct validity of written or simulation-based assessments of differential diagnosis (DDX) competencies. In 1987, a team of investigators at our institution introduced preliminary reports regarding the psychometric properties of an artificial intelligence-derived DDX assessment instrument. These investigations produced evidence of the construct validity (experts' DDX performance > novices') of the measures derived from this instrument, a linear, fuzzy set-like expert system. In this investigation, the authors used a non-linear, “Back Propagation” neural network as a DDX assessment instrument. An Acute Chest Pain knowledge base was acquired from each of twenty-four board certified emergency medicine specialists and seventy-four junior and senior medical students. The neural network used these knowledge bases to simulate and assess each subject's individual DDX performance against twenty Acute Chest Pain/Myocardial Infarction test cases. Student-t test revealed that the DDX performance of experts was significantly superior to novices ( p < .001). This finding provides converging evidence of the validity of DDX performance measures produced by both linear and non-linear, artificial intelligence-derived assessment instruments. These instruments may prove to be a useful and powerful new assessment methodology.

APA, Harvard, Vancouver, ISO, and other styles

5

Brahimi, Tayeb. "Using Artificial Intelligence to Predict Wind Speed for Energy Application in Saudi Arabia." Energies 12, no. 24 (2019): 4669. http://dx.doi.org/10.3390/en12244669.

Full text

Abstract:

Predicting wind speed for wind energy conversion systems (WECS) is an essential monitor, control, plan, and dispatch generated power and meets customer needs. The Kingdom of Saudi Arabia recently set ambitious targets in its national transformation program and Vision 2030 to move away from oil dependence and redirect oil and gas exploration efforts to other higher-value uses, chiefly meeting 10% of its energy demand through renewable energy sources. In this paper, we propose the use of the artificial neural networks (ANNs) method as a means of predicting daily wind speed in a number of locations in the Kingdom of Saudi Arabia based on multiple local meteorological measurement data provided by K.A.CARE. The suggested model is a feed-forward neural network model with the administered learning technique using a back-propagation algorithm. Results indicate that the best structure is obtained with thirty neurons in the hidden layers matching a minimum root mean square error (RMSE) and the highest correlation coefficient (R). A comparison between predicted and actual data from meteorological stations showed good agreement. A comparison between five machine learning algorithms, namely ANN, support vector machines (SVM), random tree, random forest, and RepTree revealed that random tree has low correlation and relatively high root mean square error. The significance of the present study relies on its ability to predict wind speeds, a necessary prerequisite to executing sustainable integration of wind power into Saudi Arabia’s electrical grid, assisting operators in efficiently managing generated power, and helping achieve the energy efficiency and production targets of Vision 2030.

APA, Harvard, Vancouver, ISO, and other styles

6

Xu, Hao, and Bo Yu. "Automatic thesaurus construction for spam filtering using revised back propagation neural network." Expert Systems with Applications 37, no. 1 (2010): 18–23. http://dx.doi.org/10.1016/j.eswa.2009.02.059.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Mathew Nkurlu, Baraka, Chuanbo Shen, Solomon Asante-Okyere, Alvin K. Mulashani, Jacqueline Chungu, and Liang Wang. "Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data." Energies 13, no. 3 (2020): 551. http://dx.doi.org/10.3390/en13030551.

Full text

Abstract:

Permeability is an important petrophysical parameter that controls the fluid flow within the reservoir. Estimating permeability presents several challenges due to the conventional approach of core analysis or well testing, which are expensive and time-consuming. On the contrary, artificial intelligence has been adopted in recent years in predicting reliable permeability data. Despite its shortcomings of overfitting and low convergence speed, artificial neural network (ANN) has been the widely used artificial intelligent method. Based on this, the present study conducted permeability prediction using the group method of data handling (GMDH) neural network from well log data of the West arm of the East African Rift Valley. Comparative analysis of GMDH permeability model and ANN methods of the back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were further explored. The results of the study showed that the proposed GMDH model outperformed BPNN and RBFNN as it achieved R/root mean square error (RMSE) value of 0.989/0.0241 for training and 0.868/0.204 for predicting, respectively. Sensitivity analysis carried out revealed that shale volume, standard resolution formation density, and thermal neutron porosity were the most influential well log parameters when developing the GMDH permeability model.

APA, Harvard, Vancouver, ISO, and other styles

8

Wang, Xian-Zhong, Tao Zhang, and Lei He. "Application of fuzzy adaptive back-propagation neural network in thermal conductivity gas analyzer." Neurocomputing 73, no. 4-6 (2010): 679–83. http://dx.doi.org/10.1016/j.neucom.2009.11.013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Li, Howard, Liam Paull, Yevgen Biletskiy, and Simon X. Yang. "Document Classification Using Information Theory And A Fast Back-Propagation Neural Network." Intelligent Automation & Soft Computing 16, no. 1 (2010): 25–38. http://dx.doi.org/10.1080/10798587.2010.10643061.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Yunong Zhang, Dongsheng Guo, and Zhan Li. "Common Nature of Learning Between Back-Propagation and Hopfield-Type Neural Networks for Generalized Matrix Inversion With Simplified Models." IEEE Transactions on Neural Networks and Learning Systems 24, no. 4 (2013): 579–92. http://dx.doi.org/10.1109/tnnls.2013.2238555.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "Back propagation (Artificial intelligence) Neural networks (Computer science)"

1

Chen, Peng. "Analysis of contribution rates and prediction based on back propagation neural networks." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691340.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Tien, Fang-Chih. "Using neural networks for three-dimensional measurement in stereo vision systems /." free to MU campus, to others for purchase, 1996. http://wwwlib.umi.com/cr/mo/fullcit?p9720552.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Civelek, Ferda N. (Ferda Nur). "Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278824/.

Full text

Abstract:

Representing time has been considered a general problem for artificial intelligence research for many years. More recently, the question of representing time has become increasingly important in representing human decision making process through connectionist expert systems. Because most human behaviors unfold over time, any attempt to represent expert performance, without considering its temporal nature, can often lead to incorrect results. A temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems, has been introduced. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications. A temporal backpropagation algorithm which supports the model has been developed. The model along with the temporal backpropagation algorithm makes it extremely practical to define any artificial neural network application. Also, an approach that can be followed to decrease the memory space used by weight matrix has been introduced. The algorithm was tested using a medical connectionist expert system to show how best we describe not only the disease but also the entire course of the disease. The system, first, was trained using a pattern that was encoded from the expert system knowledge base rules. Following then, series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The first series of experiments was done to determine if the training process worked as predicted. In the second series of experiments, the weight matrix in the trained system was defined as a function of time intervals before presenting the system with the learned patterns. The result of the two experiments indicate that both approaches produce correct results. The only difference between the two results was that compressing the weight matrix required more training epochs to produce correct results. To get a measure of the correctness of the results, an error measure which is the value of the error squared was summed over all patterns to get a total sum of squares.

APA, Harvard, Vancouver, ISO, and other styles

4

Traver, Michael L. "In-cylinder combustion-based virtual emissions sensing." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=459.

Full text

Abstract:

Thesis (Ph. D.)--West Virginia University, 1999.<br>Title from document title page. Document formatted into pages; contains x, 144 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 81-84).

APA, Harvard, Vancouver, ISO, and other styles

5

Sahasrabudhe, Mandar. "Neural network applications in fluid dynamics." Thesis, Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-08112002-221615.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Lin, Yu Chu. "E-government website performance evaluation based on BP neural network." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691489.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Scarborough, David J. (David James). "An Evaluation of Backpropagation Neural Network Modeling as an Alternative Methodology for Criterion Validation of Employee Selection Testing." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc277752/.

Full text

Abstract:

Employee selection research identifies and makes use of associations between individual differences, such as those measured by psychological testing, and individual differences in job performance. Artificial neural networks are computer simulations of biological nerve systems that can be used to model unspecified relationships between sets of numbers. Thirty-five neural networks were trained to estimate normalized annual revenue produced by telephone sales agents based on personality and biographic predictors using concurrent validation data (N=1085). Accuracy of the neural estimates was compared to OLS regression and a proprietary nonlinear model used by the participating company to select agents.

APA, Harvard, Vancouver, ISO, and other styles

8

Ennaji, Moulay Abderrahim. "Analyse et conception d'un réseau de neurones formels pour le filtrage d'un signal dynamique /." Thèse, Chicoutimi : Université du Québec à Chicoutimi, 1992. http://theses.uqac.ca.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Manoharan, Madhu. "Evaluation of a neural network for formulating a semi-empirical variable kernel BRDF model." Master's thesis, Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/content/templates/?a=72.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Riera, Alexis J. "Predicting permeability and flow capacity distribution with back-propagation artificial neural networks." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1309.

Full text

Abstract:

Thesis (M.S.)--West Virginia University, 2000.<br>Title from document title page. Document formatted into pages; contains xii, 86 p. : ill. (some col.), maps. Includes abstract. Includes bibliographical references (p. 61-63).

APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Back propagation (Artificial intelligence) Neural networks (Computer science)"

1

N, Sundararajan, and Foo Shou King, eds. Parallel implementations of backpropagation neural networks on transputers: A study of training set parallelism. World Scientific, 1996.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Back propagation (Artificial intelligence) Neural networks (Computer science)"

1

Raheni, T. D., and P. Thirumoorthi. "Stochastic Artificial Intelligence: Review Article." In Deterministic Artificial Intelligence. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.90003.

Full text

Abstract:

Artificial intelligence (AI) is a region of computer techniques that deals with the design of intelligent machines that respond like humans. It has the skill to operate as a machine and simulate various human intelligent algorithms according to the user’s choice. It has the ability to solve problems, act like humans, and perceive information. In the current scenario, intelligent techniques minimize human effort especially in industrial fields. Human beings create machines through these intelligent techniques and perform various processes in different fields. Artificial intelligence deals with real-time insights where decisions are made by connecting the data to various resources. To solve real-time problems, powerful machine learning-based techniques such as artificial intelligence, neural networks, fuzzy logic, genetic algorithms, and particle swarm optimization have been used in recent years. This chapter explains artificial neural network-based adaptive linear neuron networks, back-propagation networks, and radial basis networks.

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Back propagation (Artificial intelligence) Neural networks (Computer science)"

1

Nie, Peng. "A Multi-Dimension Factor Decision-Making Model Framework Using Back-Propagation Neural Networks." In International Conference on Computer Science and Artificial Intelligence (CSAI2016). WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813220294_0115.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Chang, Sheng, Jiangfeng Li, Xiaoli Jiao, and Wei Song. "Intensity Evaluation of Urban Land Use Based on Back-Propagation Artificial Neural Networks." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5364918.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!