Log in

Relevant bibliographies by topics / Multilayer perceptron network

Contents

Journal articles

Academic literature on the topic 'Multilayer perceptron network'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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 'Multilayer perceptron network.'

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 "Multilayer perceptron network"

1

Rudenko, Oleg, Oleksandr Bezsonov, and Oleksandr Romanyk. "Neural network time series prediction based on multilayer perceptron." Development Management 17, no. 1 (2019): 23–34. http://dx.doi.org/10.21511/dm.5(1).2019.03.

Full text

Abstract:

Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.

APA, Harvard, Vancouver, ISO, and other styles

2

KUKOLJ, DRAGAN D., MIROSLAVA T. BERKO-PUSIC, and BRANISLAV ATLAGIC. "Experimental design of supervisory control functions based on multilayer perceptrons." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 5 (2001): 425–31. http://dx.doi.org/10.1017/s0890060401155058.

Full text

Abstract:

This article presents the results of research concerning possibilities of applying multilayer perceptron type of neural network for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the multilayer perceptron was considered. The emphasis was put on the multilayer perceptrons' function as a state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of multilayer perceptrons were analyzed.

APA, Harvard, Vancouver, ISO, and other styles

3

Ringienė, Laura, and Gintautas Dzemyda. "Specialios struktūros daugiasluoksnis perceptronas daugiamačiams duomenims vizualizuoti." Informacijos mokslai 50 (January 1, 2009): 358–64. http://dx.doi.org/10.15388/im.2009.0.3210.

Full text

Abstract:

Pasiūlytas ir ištirtas radialinių bazinių funkcijų ir daugiasluoksnio perceptrono junginys daugiamačiams duomenis vizualizuoti. Siūlomas vizualizavimo būdas apima daugiamačių duomenų matmenų mažinimą naudojant radialines bazines funkcijas, daugiamačių duomenų suskirstymą į klasterius, klasterį charakterizuojančių skaitinių reikšmių nustatymą ir daugiamačių duomenų vizualizavimą dirbtinio neuroninio tinklo paskutiniame paslėptajame sluoksnyje.Special Multilayer Perceptron for Multidimensional Data VisualizationLaura Ringienė, Gintautas Dzemyda SummaryIn this paper a special feed forward neural network, consisting of the radial basis function layer and a multilayer perceptron is presented. The multilayer perceptron has been proposed and investigated for multidimensional data visualization. The roposedvisualization approach includes data clustering, determining the parameters of the radial basis function and forming the data set to train the multilayer perceptron. The outputs of the last hidden layer are assigned as coordinates of the visualized points.

APA, Harvard, Vancouver, ISO, and other styles

4

Araujo, P., G. Astray, J. A. Ferrerio-Lage, J. C. Mejuto, J. A. Rodriguez-Suarez, and B. Soto. "Multilayer perceptron neural network for flow prediction." J. Environ. Monit. 13, no. 1 (2011): 35–41. http://dx.doi.org/10.1039/c0em00478b.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

ANDERSEN, TIMOTHY L., and TONY R. MARTINEZ. "DMP3: A DYNAMIC MULTILAYER PERCEPTRON CONSTRUCTION ALGORITHM." International Journal of Neural Systems 11, no. 02 (2001): 145–65. http://dx.doi.org/10.1142/s0129065701000576.

Full text

Abstract:

This paper presents DMP3 (Dynamic Multilayer Perceptron 3), a multilayer perceptron (MLP) constructive training method that constructs MLPs by incrementally adding network elements of varying complexity to the network. DMP3 differs from other MLP construction techniques in several important ways, and the motivation for these differences are given. Information gain rather than error minimization is used to guide the growth of the network, which increases the utility of newly added network elements and decreases the likelihood that a premature dead end in the growth of the network will occur. The generalization performance of DMP3 is compared with that of several other well-known machine learning and neural network learning algorithms on nine real world data sets. Simulation results show that DMP3 performs better (on average) than any of the other algorithms on the data sets tested. The main reasons for this result are discussed in detail.

APA, Harvard, Vancouver, ISO, and other styles

6

Kumar, Deepak. "Power System Restoration Using Multilayer Perceptron." International Journal of Engineering, Science and Information Technology 1, no. 1 (2021): 10–14. http://dx.doi.org/10.52088/ijesty.v1i1.35.

Full text

Abstract:

In recent years, power systems are being operated nearer to their limits due to economic competition and deregulation. Also, nowadays the challenge is to include large and ever increasing amounts of decentralized generated power into the existing transmission network and at the same time comply with the electricity market transmission demands. Both factors increase the risk of blackout. After which, power needs to be restored as quickly and reliably as possible and, accordingly, detailed power system restoration plans are required. The multilayer perceptron network is chosen for a more precise examination.

APA, Harvard, Vancouver, ISO, and other styles

7

Kaur, Jatinder, Dr Mandeep Singh, Pardeep Singh Bains, and Gagandeep Singh. "Analysis of Multi layer Perceptron Network." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 2 (2013): 600–606. http://dx.doi.org/10.24297/ijct.v7i2.3462.

Full text

Abstract:

In this paper, we introduce the multilayer Perceptron (feedforward) neural network (MLPs) and used it for a function approximation. For the training of MLP, we have used back propagation algorithm principle. The main purpose of this paper lies in changing the number of hidden layers of MLP for achieving minimum value of mean square error.

APA, Harvard, Vancouver, ISO, and other styles

8

Kusumoputro, Benyamin, and Teguh P. Arsyad. "Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 3 (2005): 290–96. http://dx.doi.org/10.20965/jaciii.2005.p0290.

Full text

Abstract:

Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.

APA, Harvard, Vancouver, ISO, and other styles

9

talal gadawe, Nour, and Rafid Ahmed Khalil. "FPGA Implementation of a Multilayer Perceptron (MLP) Network." AL-Rafdain Engineering Journal (AREJ) 17, no. 1 (2009): 1–13. http://dx.doi.org/10.33899/rengj.2009.38557.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

LERNER, B., H. GUTERMAN, I. DINSTEIN, and Y. ROMEM. "HUMAN CHROMOSOME CLASSIFICATION USING MULTILAYER PERCEPTRON NEURAL NETWORK." International Journal of Neural Systems 06, no. 03 (1995): 359–70. http://dx.doi.org/10.1142/s012906579500024x.

Full text

Abstract:

A multilayer perceptron (MLP) neural network (NN) has been studied for human chromosome classification. Only 10–20 examples were required for the MLP NN to reach its ultimate performance classifying chromosomes of 5 types. The empirical dependence of the entropic error on the number of examples was found to be highly comparable to the 1/t function. The principal component analysis (PCA) was used, both for network initialization and for feature reduction purposes. The PCA demonstrated the importance of retaining most of the image information whenever small training sets are used. The MLP NN classifier outperformed the Bayes piecewise classifier for all the cases tested. The MLP classifier was found to be almost unsusceptible to the ratio of the number of training vectors to the number of features, whereas the piecewise classifier was highly dependent on this ratio.

APA, Harvard, Vancouver, ISO, and other styles

More sources

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!