Gotowe bibliografie tematyczne / ANNs

Gotowa bibliografia na temat „ANNs”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 11 września 2023

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Artykuły w czasopismach na temat "ANNs"

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Dao, Khoi Nguyen, i Phuong Ai Huynh. "Using artificial neural network in simulating of the streamflow in the Srepok watershed". Science and Technology Development Journal 19, nr 2 (30.06.2016): 114–20. http://dx.doi.org/10.32508/stdj.v19i2.796.

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In this study, artificial neural network (ANN) model was used to simulate the streamflow in the Srepok watershed, Vietnam. Correlation analysis of time series for precipitation and streamflow was employed to determine input data for the ANN model. This result indicated a significant correlation up to 2 day time lag and 1 day time lag for the precipitation and streamflow series data, respectively. According to the correlation analysis, three ANN models including ANN1, ANN2, and ANN3 were investigated. A 3-year data record for the precipitation and streamflow was used for ANN training and testing. The result of ANN training and testing showed that the ANN2 with 3 input data (P(t), P(t-1), and Q(t- 1)) gave the best simulation (NSE = 0.95 for training period and NSE = 0.96 for testing period) comparing to those of ANN1 and ANN3. In addition, the comparison of ANNs showed that the increase of the input data did not offer the better result.
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Li, Li, i Kyung Soo Jun. "A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks". Water 14, nr 9 (26.04.2022): 1393. http://dx.doi.org/10.3390/w14091393.

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Climate change is driving worsening flood events worldwide. In this study, a hybrid approach based on a combination of the optimization of a hydrodynamic model and an error correction modeling that exploit different aspects of the physical system is proposed to improve the forecasting accuracy of flood water levels. In the parameter optimization procedure for the hydrodynamic model, Manning’s roughness coefficients were estimated by considering their spatial distribution and temporal variation in unsteady flow conditions. In the following error correction procedure, the systematic errors of the optimized hydrodynamic model were captured by combining the input variable selection method using partial mutual information (PMI) and artificial neural networks (ANNs), and therefore, complementary information provided by the data was achieved. The developed ANNs were used to analyze the potential non-linear relationships between the considered state variables and simulation errors to predict systematic errors. To assess the hybrid forecasting approach (hydrodynamic model with an ANN-based error correction model), performances of the hydrodynamic model, two ANN-based water-level forecasting models (ANN1 and ANN2), and the hybrid model were compared. Regarding input candidates, ANN1 considers the historical observations only, and ANN2 considers not only the historical observations that used in ANN1 but also the prescribed boundary conditions required for the hydrodynamic forecast model. As a result, the hybrid model significantly improved the forecasting accuracy of flood water levels compared to individual models, which indicates that the hybrid model is able to take advantage of complementary strengths of both the hydrodynamic model and the ANN model. The optimization of the hydrodynamic model allowing spatially and temporally variable parameters estimated water levels with acceptable accuracy. Furthermore, the use of PMI-based input variable selection and optimized ANNs as error correction models for different sites were proven to successfully predict simulation errors in the hydrodynamic model. Hence, the parameter optimization of the hydrodynamic model coupled with error correction modeling for water level forecasting can be used to provide accurate information for flood management.
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Turco, Chiara, Marco Francesco Funari, Elisabete Teixeira i Ricardo Mateus. "Artificial Neural Networks to Predict the Mechanical Properties of Natural Fibre-Reinforced Compressed Earth Blocks (CEBs)". Fibers 9, nr 12 (1.12.2021): 78. http://dx.doi.org/10.3390/fib9120078.

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The purpose of this study is to explore Artificial Neural Networks (ANNs) to predict the compressive and tensile strengths of natural fibre-reinforced Compressed Earth Blocks (CEBs). To this end, a database was created by collecting data from the available literature. Data relating to 332 specimens (Database 1) were used for the prediction of the compressive strength (ANN1), and, due to the lack of some information, those relating to 130 specimens (Database 2) were used for the prediction of the tensile strength (ANN2). The developed tools showed high accuracy, i.e., correlation coefficients (R-value) equal to 0.97 for ANN1 and 0.91 for ANN2. Such promising results prompt their applicability for the design and orientation of experimental campaigns and support numerical investigations.
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Shafrai, Anton V., Larisa V. Permyakova, Dmitriy M. Borodulin i Irina Y. Sergeeva. "Modeling the Physiological Parameters of Brewer’s Yeast during Storage with Natural Zeolite-Containing Tuffs Using Artificial Neural Networks". Information 13, nr 11 (7.11.2022): 529. http://dx.doi.org/10.3390/info13110529.

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Various methods are used to prevent the deterioration of the biotechnological properties of brewer’s yeast during storage. This paper studied the use of artificial neural networks for the mathematical modeling of correcting the biosynthetic activity of brewer’s seed yeast of the C34 race during storage with natural minerals. The input parameters for the artificial neural networks were the suspending medium (water, beer wort, or young beer); the type of the zeolite-containing tuff from Siberian deposits; the tuff content (0.5–4% of the total volume of the suspension); and the duration of storage (3 days). The output parameters were the number of yeast cells with glycogen, budding cells, and dead cells. In the yeast stored with tuffs, the number of budding cells increased by 1.2–2.5 times, and the number of cells with glycogen increased by 9–190% compared to the control sample (without tuff). The presence of kholinskiy zeolite and shivyrtuin tuffs resulted in a significant effect. The artificial neural networks were required for solving the regression tasks and predicting the output parameters based on the input parameters. Four networks were created: ANN1 (mean relative error = 4.869%) modeled the values of all the output parameters; ANN2 (MRE = 1.8381%) modeled the number of cells with glycogen; ANN3 (MRE = 6.2905%) modeled the number of budding cells; and ANN4 (MRE = 4.2191%) modeled the number of dead cells. The optimal parameters for yeast storage were then determined. As a result, the possibility of using ANNs for mathematical modeling of undesired deviations in the physiological parameters of brewer’s seed yeast during storage with natural minerals was proven.
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Mimeche, Omar, Amir Aieb, Antonio Liotta i Khodir Madani. "A Novel Interannual Rainfall Runoff Equation Derived from Ol’Dekop’s Model Using Artificial Neural Networks". Sensors 22, nr 12 (8.06.2022): 4349. http://dx.doi.org/10.3390/s22124349.

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In water resources management, modeling water balance factors is necessary to control dams, agriculture, irrigation, and also to provide water supply for drinking and industries. Generally, conceptual and physical models present challenges to find more hydro-climatic parameters, which show good performance in the assessment of runoff in different climatic regions. Accordingly, a dynamic and reliable model is proposed to estimate inter-annual rainfall-runoff in five climatic regions of northern Algeria. This is a new improvement of Ol’Dekop’s equation, which models the residual values obtained between real and predicted data using artificial neuron networks (ANNs), namely by ANN1 and ANN2 sub-models. In this work, a set of climatic and geographical variables, obtained from 16 basins, which are inter-annual rainfall (IAR), watershed area (S), and watercourse (WC), were used as input data in the first model. Further, the ANN1 output results and De Martonne index (I) were classified, and were then processed by ANN2 to further increase reliability, and make the model more dynamic and unaffected by the climatic characteristic of the area. The final model proved the best performance in the entire region compared to a set of parametric and non-parametric water balance models used in this study, where the R2Adj obtained from each test gave values between 0.9103 and 0.9923.
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Meng, Jingfan, Huayi Wang, Jun Xu i Mitsunori Ogihara. "ONe Index for All Kernels (ONIAK)". Proceedings of the VLDB Endowment 15, nr 13 (wrzesień 2022): 3937–49. http://dx.doi.org/10.14778/3565838.3565847.

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In this work, we formulate and solve a new type of approximate nearest neighbor search (ANNS) problems called ANNS after linear transformation (ALT). In ANNS-ALT, we search for the vector (in a dataset) that, after being linearly transformed by a user-specified query matrix, is closest to a query vector. It is a very general mother problem in the sense that a wide range of baby ANNS problems that have important applications in databases and machine learning can be reduced to and solved as ANNS-ALT, or its dual that we call ANNS-ALTD. We propose a novel and computationally efficient solution, called ONe Index for All Kernels (ONIAK), to ANNS-ALT and all its baby problems when the data dimension d is not too large (say d ≤ 200). In ONIAK, a universal index is built, once and for all, for answering all future ANNS-ALT queries that can have distinct query matrices. We show by experiments that, when d is not too large, ONIAK has better query performance than linear scan on the mother problem (of ANNS-ALT), and has query performances comparable to those of the state-of-the-art solutions on the baby problems. However, the algorithmic technique behind this universal index approach suffers from a so-called dimension blowup problem that can make the indexing time prohibitively long for a large dataset. We propose a novel algorithmic technique, called fast GOE quadratic form (FGoeQF), that completely solves the (prohibitively long indexing time) fallout of the dimension blowup problem. We also propose a Johnson-Lindenstrauss transform (JLT) based ANNS-ALT (and ANNS-ALTD) solution that significantly outperforms any competitor when d is large.
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O'Reilly, G., C. C. Bezuidenhout i J. J. Bezuidenhout. "Artificial neural networks: applications in the drinking water sector". Water Supply 18, nr 6 (31.01.2018): 1869–87. http://dx.doi.org/10.2166/ws.2018.016.

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Abstract Artificial neural networks (ANNs) could be used in effective drinking water quality management. This review provides an overview about the history of ANNs and their applications and shortcomings in the drinking water sector. From the papers reviewed, it was found that ANNs might be useful modelling tools due to their successful application in areas such as pipes/infrastructure, membrane filtration, coagulation dosage, disinfection residuals, water quality, etc. The most popular ANNs applied were feed-forward networks, especially Multi-layer Perceptrons (MLPs). It was also noted that over the past decade (2006–2016), ANNs have been increasingly applied in the drinking water sector. This, however, is not the case for South Africa where the application of ANNs in distribution systems is little to non-existent. Future research should be directed towards the application of ANNs in South African distribution systems and to develop these models into decision-making tools that water purification facilities could implement.
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Gülmez, Burak, i Sinem Kulluk. "Social Spider Algorithm for Training Artificial Neural Networks". International Journal of Business Analytics 6, nr 4 (październik 2019): 32–49. http://dx.doi.org/10.4018/ijban.2019100103.

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Artificial neural networks (ANNs) are one of the most widely used techniques for generalization, classification, and optimization. ANNs are inspired from the human brain and perform some abilities automatically like learning new information and making new inferences. Back-propagation (BP) is the most common algorithm for training ANNs. But the processing of the BP algorithm is too slow, and it can be trapped into local optima. The meta-heuristic algorithms overcome these drawbacks and are frequently used in training ANNs. In this study, a new generation meta-heuristic, the Social Spider (SS) algorithm, is adapted for training ANNs. The performance of the algorithm is compared with conventional and meta-heuristic algorithms on classification benchmark problems in the literature. The algorithm is also applied to real-world data in order to predict the production of a factory in Kayseri and compared with some regression-based algorithms and ANNs models. The obtained results and comparisons on classification benchmark datasets have shown that the SS algorithm is a competitive algorithm for training ANNs. On the real-world production dataset, the SS algorithm has outperformed all compared algorithms. As a result of experimental studies, the SS algorithm is highly capable for training ANNs and can be used for both classification and regression.
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Kariri, Elham, Hassen Louati, Ali Louati i Fatma Masmoudi. "Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach". Applied Sciences 13, nr 5 (2.03.2023): 3186. http://dx.doi.org/10.3390/app13053186.

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Artificial Neural Networks (ANNs) are machine learning algorithms inspired by the structure and function of the human brain. Their popularity has increased in recent years due to their ability to learn and improve through experience, making them suitable for a wide range of applications. ANNs are often used as part of deep learning, which enables them to learn, transfer knowledge, make predictions, and take action. This paper aims to provide a comprehensive understanding of ANNs and explore potential directions for future research. To achieve this, the paper analyzes 10,661 articles and 35,973 keywords from various journals using a text-mining approach. The results of the analysis show that there is a high level of interest in topics related to machine learning, deep learning, and ANNs and that research in this field is increasingly focusing on areas such as optimization techniques, feature extraction and selection, and clustering. The study presented in this paper is motivated by the need for a framework to guide the continued study and development of ANNs. By providing insights into the current state of research on ANNs, this paper aims to promote a deeper understanding of ANNs and to facilitate the development of new techniques and applications for ANNs in the future.
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Abalo, Jesus N. "Capabilities of Computer Algorithm Utilizing Artificial Neural Networks and its Implications to Economy: A Public Policy Analysis". Proceedings of The International Halal Science and Technology Conference 14, nr 1 (10.03.2022): 83–88. http://dx.doi.org/10.31098/ihsatec.v14i1.489.

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The massive abundance of studies pertinent to Artificial Neural Networks (ANN) has produced exciting and dulcet effects on different industries and academic disciplines. Albeit the findings of the studies relating to ANNs invite potential enterprising opportunities, it is an incontestable fact that these enterprising opportunities, like fruits of the ANNs, valiantly interpose an economic threat to the working manpower. Employment retrenchment is portending as companies opt to enjoy the benefit yielded from the application and use of ANN mechanisms. ANNs will overshadow and replace the working manpower. This study is a meta-analysis that profoundly discourses on the implications of economic issues embedded in the application and use of ANNs. The findings hereof are critical and material considerations in the craft of effective public policy measures that necessarily balance the economic impact of the ANNs to working manpower. Thus this study aims to answer two primary inquiries; (1) what are the economic implications of the application and use of ANNs? and (2) what public policy measures balance the economic downsides of the application and use of ANNs?
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Rozprawy doktorskie na temat "ANNs"

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Ghosh, Ranadhir, i n/a. "A Novel Hybrid Learning Algorithm For Artificial Neural Networks". Griffith University. School of Information Technology, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030808.162355.

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Last few decades have witnessed the use of artificial neural networks (ANN) in many real-world applications and have offered an attractive paradigm for a broad range of adaptive complex systems. In recent years ANN have enjoyed a great deal of success and have proven useful in wide variety pattern recognition or feature extraction tasks. Examples include optical character recognition, speech recognition and adaptive control to name a few. To keep the pace with its huge demand in diversified application areas, many different kinds of ANN architecture and learning types have been proposed by the researchers to meet varying needs. A novel hybrid learning approach for the training of a feed-forward ANN has been proposed in this thesis. The approach combines evolutionary algorithms with matrix solution methods such as singular value decomposition, Gram-Schmidt etc., to achieve optimum weights for hidden and output layers. The proposed hybrid method is to apply evolutionary algorithm in the first layer and least square method (LS) in the second layer of the ANN. The methodology also finds optimum number of hidden neurons using a hierarchical combination methodology structure for weights and architecture. A learning algorithm has many facets that can make a learning algorithm good for a particular application area. Often there are trade offs between classification accuracy and time complexity, nevertheless, the problem of memory complexity remains. This research explores all the different facets of the proposed new algorithm in terms of classification accuracy, convergence property, generalization ability, time and memory complexity.
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Ghosh, Ranadhir. "A Novel Hybrid Learning Algorithm For Artificial Neural Networks". Thesis, Griffith University, 2003. http://hdl.handle.net/10072/365961.

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Last few decades have witnessed the use of artificial neural networks (ANN) in many real-world applications and have offered an attractive paradigm for a broad range of adaptive complex systems. In recent years ANN have enjoyed a great deal of success and have proven useful in wide variety pattern recognition or feature extraction tasks. Examples include optical character recognition, speech recognition and adaptive control to name a few. To keep the pace with its huge demand in diversified application areas, many different kinds of ANN architecture and learning types have been proposed by the researchers to meet varying needs. A novel hybrid learning approach for the training of a feed-forward ANN has been proposed in this thesis. The approach combines evolutionary algorithms with matrix solution methods such as singular value decomposition, Gram-Schmidt etc., to achieve optimum weights for hidden and output layers. The proposed hybrid method is to apply evolutionary algorithm in the first layer and least square method (LS) in the second layer of the ANN. The methodology also finds optimum number of hidden neurons using a hierarchical combination methodology structure for weights and architecture. A learning algorithm has many facets that can make a learning algorithm good for a particular application area. Often there are trade offs between classification accuracy and time complexity, nevertheless, the problem of memory complexity remains. This research explores all the different facets of the proposed new algorithm in terms of classification accuracy, convergence property, generalization ability, time and memory complexity.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information Technology
Full Text
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Lukashev, A. "Basics of artificial neural networks (ANNs)". Thesis, Київський національний університет технологій та дизайну, 2018. https://er.knutd.edu.ua/handle/123456789/11353.

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Al-Bulushi, Nabil. "Predicting reservoir properties using artificial neural networks (ANNs)". Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.498402.

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Taylor, Brent S. "Utilizing ANNs to Improve the Forecast for Tire Demand". Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1420656622.

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Cogo, Giovanni <1989&gt. "MultiLayer ANNs: predicting the S&P 500 index". Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/7670.

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Stock prediction with artificial neural network (ANN) models has been used extensively by researchers as it provides better results than other techniques. This paper presents an ANN approach to forecast the S&P 500 stock index price. First of all, an ANN-based variable selection model is presented. This model explores the relationship between some initial input variables and the closing price of the S&P 500 stock index. Furthermore, this research investigates how the training algorithm, as well as the number of neurons in the hidden layer and the distribution of the training data, affect the accuracy of the network.
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Stiubhart, Domhnall Uilleam. "An Gaidheal, a'Ghaidhlig agus a'Ghaidhealtachd anns an t-seachdamh linn deug". Thesis, University of Edinburgh, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543577.

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Anns a'cheud leth de'n trächdas, tha mi a' toirt tarraing air an t-suidheachadh ann an Eirinn, carson a ghabh na Gäidheil thall ri ideblas steidhichte cho daingeann air creideamh agus athartha, agus gu de na treithean litreachail a dh'eirich mar thoradh air an seo, nach fhaighear air an taobh seo de Shruth na Maoile. Tionndaidhear a-nuairsin a ghabhail beachd air an eachdraidh a-bhos, 's mi a'feuchainn ri barrachd solais a leigeil asteach air tachartais nan deich bliadhna air fhichead fror-thäbhachdach eadar cur-gu-buil Reachdan Idhe agus toiseach Cogaidhean nan Tri Rioghachd bho dheireadh nan 1630an air adhart. Bithidh mi a'coimhead air na h-aobharan - an dä chuid aobharan geärrthreimhseach agus aobharan fad-threimhseach -a tha air cül nan atharrachaidhean sochmhalairteach a bha a'sior sgapadh rd nam bliadhnaichean ud. 'S iad na h-atharrachaidhean seo, agus abhuil a bh'aca air saoghal nan Gäidheal bho äm Athaiseag Theärlaich II air adhart, a bhios fainear dhomh anns an därna leth de'n t-saothair. Bha an Gaidheal a'sior ghabhail barrachd de'n t-saoghal fo 'shröin, ach aig an aon äm - gu dearbh, gu ire mhöir mar thoradh air na h-atharrachaidhean ud - ghreimich e na bu theinne ri särbheachdan an t-seann shaoghail. Air cül an t-suidheachaidh seo tha iomagain fhasmhor mu na bliadhnaichean ri teachd, äm, a-reir coltais, 'nuair nach biodh röl aig na Gaidheil idir; agus cuideachd mu dhol-sios a'choluadair ghaisgeil a bha mar bhonnsteidh do'n fhein-iomhaigh aca, gu sönraichte do dh'fhein-iomhaigh nam fireannach. Gu ire co-dhiü, b'ann mar thoradh air an iomagain seo a bha aobhar nan Stiübhartach cho feillmhor a-measg nan Gäidheal anns a'cheud leth de'n ochdamh linn deug. Chunnaic mi iomchaidh dä shleachd - mu fhäs obair na creachadaireachd -a ghleidheadh 's a chur ris an trächdas mar eärr-rädh.
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Engin, Seref Naci. "Condition monitoring of rotating machinery using wavelets as pre-processor to ANNs". Thesis, University of Hertfordshire, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267440.

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Cheng, Xiaoyu. "Applications of Artificial Neural Networks (ANNs) in exploring materials property-property correlations". Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/7968.

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The discoveries of materials property-property correlations usually require prior knowledge or serendipity, the process of which can be time-consuming, costly, and labour-intensive. On the other hand, artificial neural networks (ANNs) are intelligent and scalable modelling techniques that have been used extensively to predict properties from materials’ composition or processing parameters, but are seldom used in exploring materials property-property correlations. The work presented in this thesis has employed ANNs combinatorial searches to explore the correlations of different materials properties, through which, ‘known’ correlations are verified, and ‘unknown’ correlations are revealed. An evaluation criterion is proposed and demonstrated to be useful in identifying nontrivial correlations. The work has also extended the application of ANNs in the fields of data corrections, property predictions and identifications of variables’ contributions. A systematic ANN protocol has been developed and tested against the known correlating equations of elastic properties and the experimental data, and is found to be reliable and effective to correct suspect data in a complicated situation where no prior knowledge exists. Moreover, the hardness increments of pure metals due to HPT are accurately predicted from shear modulus, melting temperature and Burgers vector. The first two variables are identified to have the largest impacts on hardening. Finally, a combined ANN-SR (symbolic regression) method is proposed to yield parsimonious correlating equations by ruling out redundant variables through the partial derivatives method and the connection weight approach, which are based on the analysis of the ANNs weight vectors. By applying this method, two simple equations that are at least as accurate as other models in providing a rapid estimation of the enthalpies of vaporization for compounds are obtained.
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Zhang, Yiming. "Applications of artificial neural networks (ANNs) in several different materials research fields". Thesis, Queen Mary, University of London, 2010. http://qmro.qmul.ac.uk/xmlui/handle/123456789/362.

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In materials science, the traditional methodological framework is the identification of the composition-processing-structure-property causal pathways that link hierarchical structure to properties. However, all the properties of materials can be derived ultimately from structure and bonding, and so the properties of a material are interrelated to varying degrees. The work presented in this thesis, employed artificial neural networks (ANNs) to explore the correlations of different material properties with several examples in different fields. Those including 1) to verify and quantify known correlations between physical parameters and solid solubility of alloy systems, which were first discovered by Hume-Rothery in the 1930s. 2) To explore unknown crossproperty correlations without investigating complicated structure-property relationships, which is exemplified by i) predicting structural stability of perovskites from bond-valence based tolerance factors tBV, and predicting formability of perovskites by using A-O and B-O bond distances; ii) correlating polarizability with other properties, such as first ionization potential, melting point, heat of vaporization and specific heat capacity. 3) In the process of discovering unanticipated relationships between combination of properties of materials, ANNs were also found to be useful for highlighting unusual data points in handbooks, tables and databases that deserve to have their veracity inspected. By applying this method, massive errors in handbooks were found, and a systematic, intelligent and potentially automatic method to detect errors in handbooks is thus developed. Through presenting these four distinct examples from three aspects of ANN capability, different ways that ANNs can contribute to progress in materials science has been explored. These approaches are novel and deserve to be pursued as part of the newer methodologies that are beginning to underpin material research.
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Książki na temat "ANNs"

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Bethan, Matthews, red. Anns an sgoil. Oxford: Heinemann, 1999.

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Sjögren, Vivi-Ann. Vivi-Anns kök. [Helsingfors]: Schildt, 2007.

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Rinehart, Joyce Gerardi. Wonderful Raggedy Anns. Atglen, PA: Schiffer Pub., 1997.

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Tam, Ka-Fai. Robot gripper control using ANNs. Manchester: UMIST, 1997.

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Reid, Dee. Mata Mòr anns a' phàirc. [Stornoway?]: PRG/Acair, 1999.

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Martin, Norma. Anns an dùthaich =: [In the country]. Edinburgh: City of Edinburgh Council, Education, 2004.

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Na teampaill: Anns na h-Eileanan an Iar. Stornoway: Acair, 1997.

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MacLeod, Finlay. Tobraichean slàinte anns na h-Eileanan an Iar. [Steòrnabhagh, Eilean Leòdhais]: Stòrlann, 2000.

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Ionnsachaidh, Comann an Luchd, red. Màiri anns na bùthan =: Mary in the shops. Inbhir-Nis [Inverness]: Comann an Luchd Ionnsachaidh, 1992.

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Spooner, Albert. I believe in angels: Memories from St Anns Well Road in bygonedays. Wymondham: Stylus, 1992.

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Części książek na temat "ANNs"

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Khan, Gul Muhammad. "Artificial Neural Network (ANNs)". W Evolution of Artificial Neural Development, 39–55. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67466-7_4.

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Zupan, Jure. "Artificial Neural Networks (ANNs)". W Chemoinformatics, 438–52. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2018. http://dx.doi.org/10.1002/9783527816880.ch11_02.

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Bourlard, Hervé A., i Nelson Morgan. "Speech Recognition Using ANNs". W Connectionist Speech Recognition, 83–114. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-3210-1_5.

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Norris, Donald J. "Predictions using ANNs and CNNs". W Machine Learning with the Raspberry Pi, 387–451. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5174-4_7.

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Kolman, Eyal, i Michael Margaliot. "Knowledge-Based Design of ANNs". W Knowledge-Based Neurocomputing: A Fuzzy Logic Approach, 59–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88077-6_6.

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Zidan, Abdel Razik Ahmed, i Mohammed Ahmed Abdel Hady. "ANNs Modeling and SPSS Analysis". W Constructed Subsurface Wetlands, 357–514. Toronto ; Waretown, NJ, USA : Apple Academic Press, 2017. |: Apple Academic Press, 2018. http://dx.doi.org/10.1201/9781315365893-8.

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Li, Jingyi, i Hong Chen. "Optimization and Prediction of Design Variables Driven by Building Energy Performance—A Case Study of Office Building in Wuhan". W Proceedings of the 2020 DigitalFUTURES, 229–42. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_22.

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AbstractThis research focuses on the energy performance of office building in Wuhan. The research explored and predicted the optimal solution of design variables by Multi-Island Genetic Algorithm (MIGA) and RBF Artificial neural networks (RBF-ANNs). Research analyzed the cluster centers of design variable by K-means cluster method. In the study, the RBF-ANNs model was established by 1,000 simulation cases. The RMSE (root mean square error) of the RBF-ANNs model in different energy aspects does not exceed 15%. Comparing to the reference case (the largest energy consumption case in the optimization), the 214 elite cases in RBF-ANNs model save at least 37.5% energy. By the cluster centers of the design variables in the elite cases, the study summarized the benchmark of 14 design variables and also suggested a building energy guidance for Wuhan office building design.
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Bellas, Francisco, Richard J. Duro i Fernando López-Peña. "Blind Signal Separation Through Cooperating ANNs". W Lecture Notes in Computer Science, 847–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552413_121.

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Stark, Lawrence W. "ANNs and MAMFs: Transparency or Opacity?" W ICANN ’94, 123–29. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2097-1_29.

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de los Mozos, M. Reyes, E. Valderrama, R. Villa, J. Roig, A. Antón i J. C. Pastor. "Detection of Glaucoma by means of ANNs". W Biological and Artificial Computation: From Neuroscience to Technology, 986–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0032559.

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Streszczenia konferencji na temat "ANNs"

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Coatanéa, Eric, Vadim Tsarkov, Siddhant Modi, Di Wu, G. Gary Wang i Hesam Jafarian. "Knowledge-Based Artificial Neural Network (KB-ANN) in Engineering: Associating Functional Architecture Modeling, Dimensional Analysis and Causal Graphs to Produce Optimized Topologies for KB-ANNs". W ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85895.

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This article documents a study on artificial neural networks (ANNs) applied to the field of engineering and more specifically a study taking advantage of prior domain knowledge of engineering systems to improve the learning capabilities of ANNs by reducing the dimensionality of the ANNs. The proposed approach ultimately leads to training a smaller ANN, offering advantage in training performances such as lower Mean Squared Error, lower cost and faster convergence. The article proposes to associate functional architecture, Pi numbers, and causal graphs and presents a design process to generate optimized knowledge-based ANN (KB-ANN) topologies. The article starts with a literature survey related to ANN and their topologies. Then, an important distinction is made between system behavior centered topologies and ANN centered topologies. The Dimensional Analysis Conceptual Modeling (DACM) framework is introduced as a way of implementing the system behavior centered topology. One case study is analyzed with the goal of defining an optimized KB-ANN topology. The study shows that the KB-ANN topology performed significantly better in term of the size of the required training set than a conventional fully-connected ANN topology. Future work will investigate the application of KB-ANNs to additive manufacturing.
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Xiangjun, Chen, i Gao Zhanfeng. "Applications of ANNs in Geotechnical Engineering". W 2007 8th International Conference on Electronic Measurement and Instruments. IEEE, 2007. http://dx.doi.org/10.1109/icemi.2007.4351003.

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King, Justin, i Ciaran Wilson. "Charge conservative FET modelling using ANNs". W 2017 12th European Microwave Integrated Circuits Conference (EuMIC). IEEE, 2017. http://dx.doi.org/10.23919/eumic.2017.8230696.

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Andrejevic Stosovic, Miona, i Vanco Litovski. "Hierarchical approach to diagnosis using ANNs". W 2008 26th International Conference on Microelectronics (MIEL 2008). IEEE, 2008. http://dx.doi.org/10.1109/icmel.2008.4559304.

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Kurian, Abhishek, i Elvin Sunildutt. "Artificial Neural Networks in Pavement Engineering: A Recent Review". W International Web Conference in Civil Engineering for a Sustainable Planet. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.112.66.

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The application of Artificial Neural Networks (ANN) in civil engineering has increased drastically in the past few years. ANN tools are nowadays used commonly in developed countries over various fields of civil engineering like geotechnical, structural, traffic, pavement engineering etc. This paper deals with the review of recent advancements and utilization of ANNs in pavement engineering. The review will focus on pavement performance prediction, maintenance strategies, distress intensity detection through deep learning techniques, pavement condition index prediction etc. The use of ANNs in pavement management systems are expected to furnish a systematic schedule and economic management strategies in the field of pavement engineering. The use of ANNs combined with deep learning techniques help to address complex problems in pavement engineering and pave the way to a sustainable future.
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Ecer, Fatih. "Comparision of Hedonic Regression Method and Artificial Neural Networks to Predict Housing Prices in Turkey". W International Conference on Eurasian Economies. Eurasian Economists Association, 2014. http://dx.doi.org/10.36880/c05.01150.

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Owner-occupied housing is both a place to live and also the most important asset in many households’ portfolio. Accurately predicting of house prices is therefore of great interest to the general public. This paper aims to compare the housing price prediction accuracies of Hedonic Model (HM) and Artificial Neural Networks (ANNs). In order to achieve this aim, two techniques’ prediction results were compared by using four performance criteria: RMSE, MAE, MAD, and Theil’s U statistic. This study uses the HM and ANNs to empirically determine the house prices in Turkey. HM is the standard technique for modeling the behavior of house prices over the past three decades and is based on micro economic theory. The non-linear relationship between house price and its determinants can be modeled by an ANN, so it is employed in this paper as an alternative method. Empirical results revealed that ANNs performed better than HM in house price predictions, indicating that ANNs could be useful for prediction of house prices. More clearly, the performance criteria from the ANNs are smaller than those from the HM by roughly 60-90%. For instance, the ANN model has about 77 percent lower RMSE, 91 percent lower MAE, 64 percent lower MAD, and 77 percent lower Theil’s U statistic than those of the HM.
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Wang, Bingsen, Jian Cao, Jue Chen, Shuo Feng i Yuan Wang. "A New ANN-SNN Conversion Method with High Accuracy, Low Latency and Good Robustness". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/342.

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Due to the advantages of low energy consumption, high robustness and fast inference speed, Spiking Neural Networks (SNNs), with good biological interpretability and the potential to be applied on neuromorphic hardware, are regarded as the third generation of Artificial Neural Networks (ANNs). Despite having so many advantages, the biggest challenge encountered by spiking neural networks is training difficulty caused by the non-differentiability of spike signals. ANN-SNN conversion is an effective method that solves the training difficulty by converting parameters in ANNs to those in SNNs through a specific algorithm. However, the ANN-SNN conversion method also suffers from accuracy degradation and long inference time. In this paper, we reanalyzed the relationship between Integrate-and-Fire (IF) neuron model and ReLU activation function, proposed a StepReLU activation function more suitable for SNNs under membrane potential encoding, and used it to train ANNs. Then we converted the ANNs to SNNs with extremely small conversion error and introduced leakage mechanism to the SNNs and get the final models, which have high accuracy, low latency and good robustness, and have achieved the state-of-the-art performance on various datasets such as CIFAR and ImageNet.
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Mechefske, Chris K., i Lingxin Li. "Induction Motor Fault Detection and Diagnosis Using Artifical Neural Networks". W ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84215.

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This paper investigates induction motor fault detection and diagnosis using Artificial Neural Networks (ANN). The ANN techniques include feedforward backpropagation networks (FFBPN) and self organizing maps (SOM), used individually and in combination. Common induction motor faults such as bearing faults, stator winding fault, unbalanced rotor and broken rotor bars are considered. The ANNs were trained and tested using dynamic measurements of stator currents and mechanical vibration signals. The effects of different network structures and the training set sizes on the performance of the ANNs are discussed. This study shows that, while the feedforward ANNs give satisfactory results and the SOMs can classify the type of motor fault during steady state working conditions, using a combination of SOM and FFBPN techniques yields superior fault detection and diagnostic accuracy. In addition, incipient motor fault detection has been investigated. The above results show that improved induction motor maintenance strategies may be possible through the use of comprehensive on-line induction motor condition monitoring and fault diagnosis systems.
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Rawlins, Timothy, Andrew Lewis, Jan Hettenhausen i Timoleon Kipouros. "Enhancing MOPSO through the guidance of ANNs". W 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889853.

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Hongyu Guo i H. L. Viktor. "Multi-view ANNs for Multi-relational Classification". W The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247280.

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Raporty organizacyjne na temat "ANNs"

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Flemming, Jens. Training ANNs. Westsächsische Hochschule Zwickau, listopad 2021. http://dx.doi.org/10.25366/2021.93.

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Lynch, G., i B. Lafrance. Bedrock geology, St. Anns Harbour, Cape Breton Island, Nova Scotia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/207788.

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam i Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, kwiecień 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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King, E. L. Seascapes of St. Anns Bank and adjoining area off Cape Breton, Nova Scotia. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2014. http://dx.doi.org/10.4095/294226.

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Alhasson, Haifa F., i Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, listopad 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic databases such as Science Direct, PubMed (MIDLINE), arXiv.org, MDPI, Nature, Google Scholar, Scopus and Wiley Online Library was conducted to identify and select the literature for this study (January 2010-January 01, 2023). It was based on the most popular image-based diagnosis targets in DFu such as segmentation, detection and classification. Various keywords were used during the identification process, including artificial intelligence in DFu, deep learning, machine learning, ANNs, CNNs, DFu detection, DFu segmentation, DFu classification, and computer-aided diagnosis.
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Riha, Joyce. Fire Ants. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.7026.

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Swank, Morgan. Parachute Dress for Anna. Ames: Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/itaa_proceedings-180814-736.

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Hoffman, Michael. Anna Held, a biography. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.3177.

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Cook, Chris B., Lance W. Vail i Duane L. Ward. North Anna Early Site Permit Water Budget Model (LakeWBT) for Lake Anna. Office of Scientific and Technical Information (OSTI), styczeń 2005. http://dx.doi.org/10.2172/15010729.

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Kenney, John J., i Walter Mann. Anna Package Specification: Case Studies. Fort Belvoir, VA: Defense Technical Information Center, październik 1991. http://dx.doi.org/10.21236/ada311117.

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