Relevant bibliographies by topics / Combination of neural networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Combination of neural networks'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Combination of neural networks"

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Zengguo Sun, Zengguo Sun, Guodong Zhao Zengguo Sun, Rafał Scherer Guodong Zhao, Wei Wei Rafał Scherer, and Marcin Woźniak Wei Wei. "Overview of Capsule Neural Networks." 網際網路技術學刊 23, no. 1 (January 2022): 033–44. http://dx.doi.org/10.53106/160792642022012301004.

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<p>As a vector transmission network structure, the capsule neural network has been one of the research hotspots in deep learning since it was proposed in 2017. In this paper, the latest research progress of capsule networks is analyzed and summarized. Firstly, we summarize the shortcomings of convolutional neural networks and introduce the basic concept of capsule network. Secondly, we analyze and summarize the improvements in the dynamic routing mechanism and network structure of the capsule network in recent years and the combination of the capsule network with other network structures. Finally, we compile the applications of capsule network in many fields, including computer vision, natural language, and speech processing. Our purpose in writing this article is to provide methods and means that can be used for reference in the research and practical applications of capsule networks.</p> <p>&nbsp;</p>
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Aladag, Cagdas Hakan, Erol Egrioglu, and Ufuk Yolcu. "Forecast Combination by Using Artificial Neural Networks." Neural Processing Letters 32, no. 3 (October 30, 2010): 269–76. http://dx.doi.org/10.1007/s11063-010-9156-7.

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Wang, Yunhong, Songde Ma, T. N. Tan, and Guosui Liu. "Combination of multiple classifiers with neural networks." IFAC Proceedings Volumes 32, no. 2 (July 1999): 5332–37. http://dx.doi.org/10.1016/s1474-6670(17)56908-6.

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Meng, Ya Feng, Sai Zhu, and Rong Li Han. "A Fault Diagnosis Method Based on Combination of Neural Network and Fault Dictionary." Advanced Materials Research 765-767 (September 2013): 2078–81. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2078.

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Neural network and Fault dictionary are two kinds of very useful fault diagnosis method. But for large scale and complex circuits, the fault dictionary is huge, and the speed of fault searching affects the efficiency of real-time diagnosing. When the fault samples are few, it is difficulty to train the neural network, and the trained neural network can not diagnose the entire faults. In this paper, a new fault diagnosis method based on combination of neural network and fault dictionary is introduced. The fault dictionary with large scale is divided into several son fault dictionary with smaller scale, and the search index of the son dictionary is organized with the neural networks trained with the son fault dictionary. The complexity of training neural network is reduced, and this method using the neural networks ability that could accurately describe the relation between input data and corresponding goal organizes the index in a multilayer binary tree with many neural networks. Through this index, the seeking scope is reduced greatly, the searching speed is raised, and the efficiency of real-time diagnosing is improved. At last, the validity of the method is proved by the experimental results.
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Iosifidis, Alexandros, Anastasios Tefas, and Ioannis Pitas. "Human Action Recognition Based on Multi-View Regularized Extreme Learning Machine." International Journal on Artificial Intelligence Tools 24, no. 05 (October 2015): 1540020. http://dx.doi.org/10.1142/s0218213015400205.

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In this paper, we employ multiple Single-hidden Layer Feedforward Neural Networks for multi-view action recognition. We propose an extension of the Extreme Learning Machine algorithm that is able to exploit multiple action representations and scatter information in the corresponding ELM spaces for the calculation of the networks’ parameters and the determination of optimized network combination weights. The proposed algorithm is evaluated by using two state-of-the-art action video representation approaches on five publicly available action recognition databases designed for different application scenarios. Experimental comparison of the proposed approach with three commonly used video representation combination approaches and relating classification schemes illustrates that ELM networks employing a supervised view combination scheme generally outperform those exploiting unsupervised combination approaches, as well as that the exploitation of scatter information in ELM-based neural network training enhances the network’s performance.
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Smith, Lauren C., and Adam Kimbrough. "Leveraging Neural Networks in Preclinical Alcohol Research." Brain Sciences 10, no. 9 (August 21, 2020): 578. http://dx.doi.org/10.3390/brainsci10090578.

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Alcohol use disorder is a pervasive healthcare issue with significant socioeconomic consequences. There is a plethora of neural imaging techniques available at the clinical and preclinical level, including magnetic resonance imaging and three-dimensional (3D) tissue imaging techniques. Network-based approaches can be applied to imaging data to create neural networks that model the functional and structural connectivity of the brain. These networks can be used to changes to brain-wide neural signaling caused by brain states associated with alcohol use. Neural networks can be further used to identify key brain regions or neural “hubs” involved in alcohol drinking. Here, we briefly review the current imaging and neurocircuit manipulation methods. Then, we discuss clinical and preclinical studies using network-based approaches related to substance use disorders and alcohol drinking. Finally, we discuss how preclinical 3D imaging in combination with network approaches can be applied alone and in combination with other approaches to better understand alcohol drinking.
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Cherepanova, V. О., and I. V. Sylka. "Optimizing the Intellectual Property Management in Accordance with a Process-Functional Approach." Business Inform 9, no. 524 (2021): 41–51. http://dx.doi.org/10.32983/2222-4459-2021-9-41-51.

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The article is aimed at developing a way to optimize the management of intellectual property (IP) objects by a process-functional approach based on the use of neural networks in combination with planning networks in conditions of uncertainty. When analyzing the works of various scholars, conceptual approaches to the formation of IP management according to both the process and the functional approaches to management were considered. The use of artificial neural networks in intellectual property management at industrial enterprises in combination with network planning in conditions of uncertainty is systematized. Neural networks consist of different architectures, but to manage intellectual property it is advisable to use either Self-Organizing Maps (SOM) by Kohonen, or Generative Pre-trained Transformer 3 (GPT-3), or Rumelhart Multilayer Perceptron, or an combination of the above. It is proved that the proposed scientific approach (instrumentarium) in the form of neural networks and network planning allows reducing the time for implementation of works related to the management of intellectual property at industrial enterprises on the grounds of a process-functional approach. Based on the carried out study, the computation of spent time was carried out, which confirmed the efficiency of the implementation of neural networks in combination with network schedule for the management of intellectual property in industrial enterprises. Prospects for further research in this direction are the development and construction of a universal instrument using neural networks and network schedule. Further development of intellectual property management will increase production efficiency and profitability of enterprises.
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Foon, See Lee, Nazira Anisa Rahim, Ahmad Zainal, and Zhang Jie. "Selective combination in multiple neural networks prediction using independent component regression approach." Chemical Engineering Research Bulletin 19 (September 10, 2017): 12. http://dx.doi.org/10.3329/cerb.v19i0.33772.

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<p>Biological processes are highly nonlinear in nature and difficult to represent accurately by simple mathematical models. However, this problem can be solved by using neural network. Neural network is a prominent modeling tool especially when it comes to intricate process such as biological process. In this paper, a multiple single hidden layer with ten hidden neurons Feedforward Artificial Neural Network (FANN) was used to model the complex and dynamic relationships between the input (dilution rate, D) and outputs (conversion, y and dimensionless temperature value, θ) for the reactive biological process. Levenberg-Marquardt Backpropagation training method was used. The multiple neural networks predicted outputs were then combined through three different methods which area simple averaging, Principal Component Regression (PCR) and Independent Component Regression (ICR). Multiple neural networks which were created by the bootstrap approach help improved single neural network performance as well as the model robustness for nonlinear process modeling. Comparison was made between the three methods. The result showed that ICR is slightly superior between the three methods especially in noise level 1,2 and 3, however ICR slightly suffer in noise level 4 and 5. This is due to the independent component regression used the latent factors and non-Gaussian distribution of y and θ values for the combination.</p><p>Chemical Engineering Research Bulletin 19(2017) 12-19</p>
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Lézoray, Olivier, and Hubert Cardot. "Comparing Combination Rules of Pairwise Neural Networks Classifiers." Neural Processing Letters 27, no. 1 (November 4, 2007): 43–56. http://dx.doi.org/10.1007/s11063-007-9058-5.

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Li, Yi Bing, and Fei Pan. "Study on the Combination of SOM and K-Means Algorithms in Manufacturing Process Quality Control." Applied Mechanics and Materials 427-429 (September 2013): 1315–18. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1315.

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Nowadays, customers are seeking products of high quality and low cost. The use of neural networks in quality control has been a popular research topic over the last decade. An adaptive self-organizing mapping (SOM) neural network algorithm is proposed to overcome the shortages of traditional neural networks in this paper. In order to improve the classification effectiveness of SOM neural network, this paper designs an improved SOM neural network, which combined the SOM and K-means algorithms. The flow of combination of SOM and K-means algorithms was analyzed in this paper. And the case study of cement slide shoe bearing in manufacturing process was also given to illustrate the feasible and effective.
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Dissertations / Theses on the topic "Combination of neural networks"

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Morabito, David L. "Statistical mechanics of neural networks and combinatorial opimization problems /." Online version of thesis, 1991. http://hdl.handle.net/1850/11089.

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Korn, Stefan. "The combination of AI modelling techniques for the simulation of manufacturing processes." Thesis, Glasgow Caledonian University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263139.

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Amanzadi, Amirhossein. "Predicting safe drug combinations with Graph Neural Networks (GNN)." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446691.

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Many people - especially during their elderly - consume multiple drugs for the treatment of complex or co-existing diseases. Identifying side effects caused by polypharmacy is crucial for reducing mortality and morbidity of the patients which will lead to improvement in their quality of life. Since there is immense space for possible drug combinations, it is infeasible to examine them entirely in the lab. In silico models can offer a convenient solution, however, due to the lack of a sufficient amount of homogenous data it is difficult to develop both reliable and scalable models in its ability to accurately predict Polypharmacy Side Effect. Recent advancement in the field of representational learning has utilized the power of graph networks to harmonize information from the heterogeneous biological databases and interactomes. This thesis takes advantage of those techniques and incorporates them with the state-of-the-art Graph Neural Network algorithms to implement a Deep learning pipeline capable of predicting the Adverse Drug Reaction of any given paired drug combinations.
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Freitas, Paulo Sérgio Abreu. "The combination of neural estimates in prediction and decision problems." Doctoral thesis, Universidade de Lisboa: Faculdade de Ciências, 2008. http://hdl.handle.net/10400.13/98.

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In this dissertation, different ways of combining neural predictive models or neural-based forecasts are discussed. The proposed approaches consider mostly Gaussian radial basis function networks, which can be efficiently identified and estimated through recursive/adaptive methods. Two different ways of combining are explored to get a final estimate – model mixing and model synthesis –, with the aim of obtaining improvements both in terms of efficiency and effectiveness. In the context of model mixing, the usual framework for linearly combining estimates from different models is extended, to deal with the case where the forecast errors from those models are correlated. In the context of model synthesis, and to address the problems raised by heavily nonstationary time series, we propose hybrid dynamic models for more advanced time series forecasting, composed of a dynamic trend regressive model (or, even, a dynamic harmonic regressive model), and a Gaussian radial basis function network. Additionally, using the model mixing procedure, two approaches for decision-making from forecasting models are discussed and compared: either inferring decisions from combined predictive estimates, or combining prescriptive solutions derived from different forecasting models. Finally, the application of some of the models and methods proposed previously is illustrated with two case studies, based on time series from finance and from tourism.
Orientador: António José Lopes Rodrigues
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Yang, Shuang. "Multistage neural network ensemble : adaptive combination of ensemble results." Thesis, London Metropolitan University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425920.

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Torres, Sospedra Joaquín. "Ensembles of Artificial Neural Networks: Analysis and Development of Design Methods." Doctoral thesis, Universitat Jaume I, 2011. http://hdl.handle.net/10803/48638.

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This thesis is focused on the analysis and development of Ensembles of Neural Networks. An ensemble is a system in which a set of heterogeneous Artificial Neural Networks are generated in order to outperform the Single network based classifiers. However, this proposed thesis differs from others related to ensembles of neural networks [1, 2, 3, 4, 5, 6, 7] since it is organized as follows.

In this thesis, firstly, an ensemble methods comparison has been introduced in order to provide a rank-based list of the best ensemble methods existing in the bibliography. This comparison has been split into two researches which represents two chapters of the thesis.

Moreover, there is another important step related to the ensembles of neural networks which is how to combine the information provided by the neural networks in the ensemble. In the bibliography, there are some alternatives to apply in order to get an accurate combination of the information provided by the heterogeneous set of networks. For this reason, a combiner comparison has also been introduced in this thesis.

Furthermore, Ensembles of Neural Networks is only a kind of Multiple Classifier System based on neural networks. However, there are other alternatives to generate MCS based on neural networks which are quite different to Ensembles. The most important systems are Stacked Generalization and Mixture of Experts. These two systems will be also analysed in this thesis and new alternatives are proposed.

One of the results of the comparative research developed is a deep understanding of the field of ensembles. So new ensemble methods and combiners can be designed after analyzing the results provided by the research performed. Concretely, two new ensemble methods, a new ensemble methodology called Cross-Validated Boosting and two reordering algorithms are proposed in this thesis. The best overall results are obtained by the ensemble methods proposed.

Finally, all the experiments done have been carried out on a common experimental setup. The experiments have been repeated ten times on nineteen different datasets from the UCI repository in order to validate the results. Moreover, the procedure applied to set up specific parameters is quite similar in all the experiments performed.

It is important to conclude by remarking that the main contributions are:

1) An experimental setup to prepare the experiments which can be applied for further comparisons. 2) A guide to select the most appropriate methods to build and combine ensembles and multiple classifiers systems. 3) New methods proposed to build ensembles and other multiple classifier systems.

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Henry, Timothy G. "Generalization of deep neural networks to unseen attribute combinations." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129905.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 71-73).
Visual understanding results from a combined understanding of primitive visual attributes such as color, texture, and shape. This allows humans and other primates to generalize their understanding of objects to new combinations of attributes. For instance, one can understand that a pink elephant is an elephant even if they have never seen this particular combination of color and shape before. However, is it the case that deep neural networks (DNNs) are able to generalize to such novel combinations in object recognition or other related vision tasks? This thesis demonstrates that (1) the ability of DNNs to generalize to unseen attribute combinations increases with the increased diversity of combinations seen in training as a percentage of the total combination space, (2) this effect is largely independent of the specifics of the DNN architecture used, (3) while single-task and multi-task formulations of supervised attribute classification problems may lead to similar performance on seen combinations, single-task formulations have a superior ability to generalize to unseen combinations, and (4) DNNs demonstrating the ability to generalize well in this setting learn to do so by leveraging emergent hidden units that exhibit properties of attribute selectivity and invariance.
by Timothy G. Henry.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Zhao, Yi. "Combination of Wireless sensor network and artifical neuronal network : a new approach of modeling." Thesis, Toulon, 2013. http://www.theses.fr/2013TOUL0013/document.

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Face à la limitation de la modélisation paramétrique, nous avons proposé dans cette thèse une procédure standard pour combiner les données reçues a partir de Réseaux de capteurs sans fils (WSN) pour modéliser a l'aide de Réseaux de Neurones Artificiels (ANN). Des expériences sur la modélisation thermique ont permis de démontrer que la combinaison de WSN et d'ANN est capable de produire des modèles thermiques précis. Une nouvelle méthode de formation "Multi-Pattern Cross Training" (MPCT) a également été introduite dans ce travail. Cette méthode permet de fusionner les informations provenant de différentes sources de données d'entraînements indépendants (patterns) en un seul modèle ANN. D'autres expériences ont montré que les modèles formés par la méthode MPCT fournissent une meilleure performance de généralisation et que les erreurs de prévision sont réduites. De plus, le modèle de réseau neuronal basé sur la méthode MPCT a montré des avantages importants dans le multi-variable Model Prédictive Control (MPC). Les simulations numériques indiquent que le MPC basé sur le MPCT a surpassé le MPC multi-modèles au niveau de l'efficacité du contrôle
A Wireless Sensor Network (WSN) consisting of autonomous sensor nodes can provide a rich stream of sensor data representing physical measurements. A well built Artificial Neural Network (ANN) model needs sufficient training data sources. Facing the limitation of traditional parametric modeling, this paper proposes a standard procedure of combining ANN and WSN sensor data in modeling. Experiments on indoor thermal modeling demonstrated that WSN together with ANN can lead to accurate fine grained indoor thermal models. A new training method "Multi-Pattern Cross Training" (MPCT) is also introduced in this work. This training method makes it possible to merge knowledge from different independent training data sources (patterns) into a single ANN model. Further experiments demonstrated that models trained by MPCT method shew better generalization performance and lower prediction errors in tests using different data sets. Also the MPCT based Neural Network Model has shown advantages in multi-variable Neural Network based Model Predictive Control (NNMPC). Software simulation and application results indicate that MPCT implemented NNMPC outperformed Multiple models based NNMPC in online control efficiency
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Alani, Shayma. "Design of intelligent ensembled classifiers combination methods." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/12793.

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Classifier ensembling research has been one of the most active areas of machine learning for a long period of time. The main aim of generating combined classifier ensembles is to improve the prediction accuracy in comparison to using an individual classifier. A combined classifiers ensemble can improve the prediction results by compensating for the individual classifier weaknesses in certain areas and benefiting from better accuracy of the other ensembles in the same area. In this thesis, different algorithms are proposed for designing classifier ensemble combiners. The existing methods such as averaging, voting, weighted average, and optimised weighted method does not increase the accuracy of the combiner in comparison to the proposed advanced methods such as genetic programming and the coalition method. The different methods are studied in detail and analysed using different databases. The aim is to increase the accuracy of the combiner in comparison to the standard stand-alone classifiers. The proposed methods are based on generating a combiner formula using genetic programming, while the coalition is based on estimating the diversity of the classifiers such that a coalition is generated with better prediction accuracy. Standard accuracy measures are used, namely accuracy, sensitivity, specificity and area under the curve, in addition to training error accuracies such as the mean square error. The combiner methods are compared empirically with several stand-alone classifiers using neural network algorithms. Different types of neural network topologies are used to generate different models. Experimental results show that the combiner algorithms are superior in creating the most diverse and accurate classifier ensembles. Ensembles of the same models are generated to boost the accuracy of a single classifier type. An ensemble of 10 models of different initial weights is used to improve the accuracy. Experiments show a significant improvement over a single model classifier. Finally, two combining methods are studied, namely the genetic programming and coalition combination methods. The genetic programming algorithm is used to generate a formula for the classifiers’ combinations, while the coalition method is based on a simple algorithm that assigns linear combination weights based on the consensus theory. Experimental results of the same databases demonstrate the effectiveness of the proposed methods compared to conventional combining methods. The results show that the coalition method is better than genetic programming.
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Huhtinen, J. (Jouni). "Utilization of neural network and agent technology combination for distributed intelligent applications and services." Doctoral thesis, University of Oulu, 2005. http://urn.fi/urn:isbn:9514278550.

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Abstract The use of agent systems has increased enormously, especially in the field of mobile services. Intelligent services have also increased rapidly in the web. In this thesis, the utilization of software agent technology in mobile services and decentralized intelligent services in the multimedia business is introduced and described. Both Genie Agent Architecture (GAA) and Decentralized International and Intelligent Software Architecture (DIISA) are described. The common problems in decentralized software systems are lack of intelligence, communication of software modules and system learning. Another problem is the personalization of users and services. A third problem is the matching of users and service characteristics in web application level in a non-linear way. In this case it means that web services follow human steps and are capable of learning from human inputs and their characteristics in an intelligent way. This third problem is addressed in this thesis and solutions are presented with two intelligent software architectures and services. The solutions of the thesis are based on a combination of neural network and agent technology. To be more specific, solutions are based on an intelligent agent which uses certain black box information like Self-Organized Map (SOM). This process is as follows; information agents collect information from different sources like the web, databases, users, other software agents and the environment. Information is filtered and adapted for input vectors. Maps are created from a data entry of an SOM. Using maps is very simple, input forms are completed by users (automatically or manually) or user agents. Input vectors are formed again and sent to a certain map. The map gives several outputs which are passed through specific algorithms. This information is passed to an intelligent agent. The needs for web intelligence and knowledge representation serving users is a current issue in many business solutions. The main goal is to enable this by means of autonomous agents which communicate with each other using an agent communication language and with users using their native languages via several communication channels.
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Books on the topic "Combination of neural networks"

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IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (1st 2000 San Antonio, Tex.). 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks: Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks : May 11-13, 2000, the Gunter Hotel, San Antonio, TX, USA. Piscataway, N.J: IEEE, 2000.

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COGANN-92 (1992 Baltimore, Md.). COGANN-92, International Workshop on Combinations of Genetic Algorithms and Neural Networks, June 6, 1992, Baltimore, Maryland. Edited by Whitley L. Darrell, Schaffer J. David, IEEE Neural Networks Council, and International Society for Genetic Algorithms. Los Alamitos, Calif: IEEE Computer Society Press, 1992.

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Dominique, Valentin, and Edelman Betty, eds. Neural networks. Thousand Oaks, Calif: Sage Publications, 1999.

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Rojas, Raúl. Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4.

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Müller, Berndt, Joachim Reinhardt, and Michael T. Strickland. Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57760-4.

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Almeida, Luis B., and Christian J. Wellekens, eds. Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52255-7.

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Davalo, Eric, and Patrick Naïm. Neural Networks. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-12312-4.

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Müller, Berndt, and Joachim Reinhardt. Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97239-3.

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Neural networks. New York: Palgrave, 2000.

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Abdi, Hervé, Dominique Valentin, and Betty Edelman. Neural Networks. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1999. http://dx.doi.org/10.4135/9781412985277.

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Book chapters on the topic "Combination of neural networks"

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Bellot, Pau, and Patrick E. Meyer. "Efficient Combination of Pairwise Feature Networks." In Neural Connectomics Challenge, 85–93. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53070-3_7.

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Biggio, Battista, Giorgio Fumera, and Fabio Roli. "Bayesian Linear Combination of Neural Networks." In Innovations in Neural Information Paradigms and Applications, 201–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04003-0_9.

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Englund, Cristofer, and Antanas Verikas. "A SOM Based Model Combination Strategy." In Advances in Neural Networks — ISNN 2005, 461–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427391_73.

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Kobos, Mateusz, and Jacek Mańdziuk. "Classification Based on Combination of Kernel Density Estimators." In Artificial Neural Networks – ICANN 2009, 125–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_13.

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Wersing, Heiko, and Edgar Körner. "Unsupervised Learning of Combination Features for Hierarchical Recognition Models." In Artificial Neural Networks — ICANN 2002, 1225–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_198.

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Martín-Merino, Manuel. "Learning a Combination of Heterogeneous Dissimilarities from Incomplete Knowledge." In Artificial Neural Networks – ICANN 2010, 62–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15825-4_7.

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Straszecka, Ewa, and Joanna Straszecka. "Membership Functions as Combination of Expert’s Knowledge with Population Information." In Neural Networks and Soft Computing, 322–27. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1902-1_47.

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Benmokhtar, Rachid, and Benoit Huet. "Classifier Fusion: Combination Methods For Semantic Indexing in Video Content." In Artificial Neural Networks – ICANN 2006, 65–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840930_7.

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Krawczyk, Bartosz, and Michał Woźniak. "Untrained Method for Ensemble Pruning and Weighted Combination." In Advances in Neural Networks – ISNN 2014, 358–65. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12436-0_40.

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Mohammed, Hussein Syed, James Leander, Matthew Marbach, and Robi Polikar. "Can AdaBoost.M1 Learn Incrementally? A Comparison to Learn + + Under Different Combination Rules." In Artificial Neural Networks – ICANN 2006, 254–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_27.

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Conference papers on the topic "Combination of neural networks"

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Freitas, Cinthia O. A., Joao M. Carvalho, Jose J. Oliveira, Simone B. K. Aires, and Robert Sabourin. "Distance-based Disagreement Classifiers Combination." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371390.

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Kassem, Ayman H., and Ihab G. Adam. "Optimizing Neural Networks for Leak Monitoring in Pipelines." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-3005.

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Feedforward neural networks can be used for nonlinear dynamic modeling. Although the basic principle of employing such networks is straightforward, the problem of selecting the training data set and the network topology is not a trivial task. This paper examines the use of genetic algorithm optimization techniques to optimize the neural network. The paper presents the results of studies on the effect of number of neurons and input combination method on the performance of neural networks and the application of this study to improve leak monitoring in pipelines. The neural networks examined in this study do not use the sensor reading directly as in conventional neural networks but combine it using polynomial type laws to produce hybrid inputs. The optimization technique tries to find the best polynomial laws (input combination) to reduce network size, head variation effect, and optimize network performance. The resulting networks show superior performance and use fewer numbers of neurons.
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Krawczyk, Bartosz, and Michal Wozniak. "New untrained aggregation methods for classifier combination." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889810.

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Breker, Sebastian, and Bernhard Sick. "Combination of uncertain ordinal expert statements: The combination rule EIDMR and its application to low-voltage grid classification with SVM." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727467.

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C. Prudencio, Ricardo, and Teresa Ludermir. "LearningWeights for Linear Combination of Forecasting Methods." In 2006 Ninth Brazilian Symposium on Neural Networks (SBRN'06). IEEE, 2006. http://dx.doi.org/10.1109/sbrn.2006.25.

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Lahroodi, Mahmood, and A. A. Mozafari. "Combination of Neural Networks and State Vector Feedback Adaptive Control (SVFAC) Technique to Control the Gas Turbine Combustor." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15764.

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Neural networks have been applied very successfully in the identification and control of dynamic systems. When designing a control system to ensure the safe and automatic operation of the gas turbine combustor, it is necessary to be able to predict temperature and pressure levels and outlet flow rate throughout the gas turbine combustor to use them for selection of control parameters. This paper describes a nonlinear SVFAC controller scheme for gas turbine combustor. In order to achieve the satisfied control performance, we have to consider the affection of nonlinear factors contained in controller. The neural network controller learns to produce the input selected by the optimization process. The controller is adaptively trained to force the plant output to track a reference output. Proposed Adaptive control system configuration uses two neural networks: a controller network and a model network. The model network is used to predict the effect of controller changes on plant output, which allows the updating of controller parameters. This paper presents the new adaptive SFVC controller using neural networks with compensation for nonlinear plants. The control performance of designed controller is compared with inverse control method and results have shown that the proposed method has good performance for nonlinear plants such as gas turbine combustor.
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Wang, Xiuying, Changliang Li, Zhijun Zheng, and Bo Xu. "Paraphrase Recognition via Combination of Neural Classifier and Keywords." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489222.

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Yazhi Gao, Wenge Rong, Yikang Shen, and Zhang Xiong. "Convolutional Neural Network based sentiment analysis using Adaboost combination." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727352.

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Mayhua-Lopez, Efrain, Vanessa Gomez-Verdejo, and Anibal R. Figueiras-Vidal. "Improving boosting performance with a local combination of learners." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596317.

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Hu, Junlin, and Ping Guo. "Learning multiple pooling combination for image classification." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252840.

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Reports on the topic "Combination of neural networks"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3743.

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In this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a sufficient number of solutions and algorithms for training, designing and testing various types of neural networks. Software architecture is presented using a procedural-object approach. Because there is no need to save intermediate result of the program (after learning entire perceptron is stored in the file), all the progress of learning is stored in the normal files on hard disk.
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Renz, Manuel. B-jet and c-jet identification with Neural Networks as well as combination of multivariate analyses for the search for of multivariate analyses for the search for single top-quark production. Office of Scientific and Technical Information (OSTI), June 2008. http://dx.doi.org/10.2172/957074.

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Johnson, John L., and C. C. Sung. Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada222110.

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Smith, Patrick I. Neural Networks. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/815740.

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Holder, Nanette S. Introduction to Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada248258.

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Wiggins, Vince L., Larry T. Looper, and Sheree K. Engquist. Neural Networks: A Primer. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada235920.

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Abu-Mostafa, Yaser S., and Amir F. Atiya. Theory of Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada253187.

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Alltop, W. O. Piecewise Linear Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada265031.

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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures. We experimentally validated our method with different deep network backbones (AlexNet-small, Resnet-20, Resnet-50) on different datasets (SVHN, Cifar-10, ImageNet) and observed consistent results.
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