Relevant bibliographies by topics / ANN – Artificial Neural Networks

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  2. Dissertations / Theses
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Academic literature on the topic 'ANN – Artificial Neural Networks'

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Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "ANN – Artificial Neural Networks"

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Çelik, Şenol. "MODELING AVOCADO PRODUCTION IN MEXICO WITH ARTIFICIAL NEURAL NETWORKS." Engineering and Technology Journal 07, no. 10 (2022): 1605–9. http://dx.doi.org/10.47191/etj/v7i10.08.

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An Artificial Neural Network (ANN) model was created in this research to estimate and predict the amount of avocado production in Mexico. In the development of the ANN model, the years that are time variable were used as the input parameter, and the avocado production amount (tons) was used as the output parameter. The research data includes avocado production in Mexico for 1961-2020 period. Mean Squared Error (MSE) and Mean Absolut Error (MAE) statistics were calculated using hyperbolic tangent activation function to determine the appropriate model. ANN model is a network architecture with 12 hidden layers, 12 process elements (12-12-1) and Levenberg-Marquardt back propagation algorithm. The amount of avocado production was estimated between 2021 and 2030 with the ANN. As a result of the prediction, it is expected that the amount of avocado production for the period 2021-2030 will be between 2,410,741-2,502,302 tons.
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TAMBOURATZIS, TATIANA. "STRING MATCHING ARTIFICIAL NEURAL NETWORKS." International Journal of Neural Systems 11, no. 05 (2001): 445–53. http://dx.doi.org/10.1142/s0129065701000874.

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Three artificial neural networks (ANNs) are proposed for solving a variety of on- and off-line string matching problems. The ANN structure employed as the building block of these ANNs is derived from the harmony theory (HT) ANN, whereby the resulting string matching ANNs are characterized by fast match-mismatch decisions, low computational complexity, and activation values of the ANN output nodes that can be used as indicators of substitution, insertion (addition) and deletion spelling errors.
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Mahat, Norpah, Nor Idayunie Nording, Jasmani Bidin, Suzanawati Abu Hasan, and Teoh Yeong Kin. "Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance." Journal of Computing Research and Innovation 7, no. 1 (2022): 29–38. http://dx.doi.org/10.24191/jcrinn.v7i1.264.

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Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the second period grade while one target output is used which is the final grade. This study also aims to identify which training function is the best among three Feed-Forward Neural Networks known as Network1, Network2 and Network3. Three types of training functions have been selected in this study, which are Levenberg-Marquardt (TRAINLM), Gradient descent with momentum (TRAINGDM) and Gradient descent with adaptive learning rate (TRAINGDA). Each training function will be compared based on Performance value, correlation coefficient, gradient and epoch. MATLAB R2020a was used for data processing. The results show that the TRAINLM function is the most suitable function in predicting mathematics students’ performance because it has a higher correlation coefficient and a lower Performance value.
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Şenol, Çelik. "Modeling Avocado Production in Mexico with Artificial Neural Networks." Engineering and Technology Journal 07, no. 10 (2022): 1605–9. https://doi.org/10.5281/zenodo.7251495.

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An Artificial Neural Network (ANN) model was created in this research to estimate and predict the amount of avocado production in Mexico. In the development of the ANN model, the years that are time variable were used as the input parameter, and the avocado production amount (tons) was used as the output parameter. The research data includes avocado production in Mexico for 1961-2020 period.  Mean Squared Error (MSE) and Mean Absolut Error (MAE) statistics were calculated using hyperbolic tangent activation function to determine the appropriate model. ANN model is a network architecture with 12 hidden layers, 12 process elements (12-12-1) and Levenberg-Marquardt back propagation algorithm. The amount of avocado production was estimated between 2021 and 2030 with the ANN. As a result of the prediction, it is expected that the amount of avocado production for the period 2021-2030 will be between 2,410,741-2,502,302 tons. 
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Dawson, C. W., and R. L. Wilby. "Hydrological modelling using artificial neural networks." Progress in Physical Geography: Earth and Environment 25, no. 1 (2001): 80–108. http://dx.doi.org/10.1177/030913330102500104.

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This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, characterized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conventional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological ‘rules’ from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.
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Doroshenko, Anna. "Applying Artificial Neural Networks In Construction." E3S Web of Conferences 143 (2020): 01029. http://dx.doi.org/10.1051/e3sconf/202014301029.

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Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.
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Aziz, Mustafa Nizamul. "A Review on Artificial Neural Networks and its’ Applicability." Bangladesh Journal of Multidisciplinary Scientific Research 2, no. 1 (2020): 48–51. http://dx.doi.org/10.46281/bjmsr.v2i1.609.

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The field of artificial neural networks (ANN) started from humble beginnings in the 1950s but got attention in the 1980s. ANN tries to emulate the neural structure of the brain, which consists of several thousand cells, neuron, which is interconnected in a large network. This is done through artificial neurons, handling the input and output, and connecting to other neurons, creating a large network. The potential for artificial neural networks is considered to be huge, today there are several different uses for ANN, ranging from academic research in such fields as mathematics and medicine to business-based purposes and sports prediction. The purpose of this paper is to give words to artificial neural networks and to show its applicability. Documents analysis was used here as the data collection method. The paper figured out network structures, steps for constructing an ANN, architectures, and learning algorithms.
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Wang, Shuihua, Huiling Chen, and Yudong Zhang. "Bionic Artificial Neural Networks in Medical Image Analysis." Biomimetics 8, no. 2 (2023): 211. http://dx.doi.org/10.3390/biomimetics8020211.

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Selitskiy, Stanislav, and Natalya Selitskaya. "Activation Functions Study for the Trustworthiness Supervisor Artificial Neural Networks." Journal of Image and Graphics 12, no. 3 (2024): 269–75. http://dx.doi.org/10.18178/joig.12.3.269-275.

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Examining and potentially adjusting one’s cognitive processes in response to dissatisfaction with one’s performance is a fundamental aspect of intelligence. Remarkably, such sophisticated abstract concepts necessary for achieving Artificial General Intelligence can be effectively incorporated into basic Machine Learning algorithms. In this study, we introduce a method for replicating self-awareness through a supervisory Artificial Neural Network (ANN), which monitors patterns in the activation functions of an underlying ANN to identify signs of substantial uncertainty within the underlying ANN and, consequently, the reliability of its predictions. The underlying ANN in this context is a Convolutional Neural Network (CNN) ensemble primarily utilized for tasks related to facial recognition and facial expression analysis. We evaluate the performance of the supervisory ANNs using various activation functions as they learn to gauge the dependability of predictions made by the Inception v3 CNN ensemble. To conduct computational experiments, we employ a facial data set that incorporates makeup and occlusion factors. These experiments are designed to mimic real-world conditions where the training data set exclusively consists of images without makeup or occlusion, while the test data set comprises images featuring makeup and occlusion. This partitioning ensures the model is tested under challenging out-of-training data distribution scenarios.
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Cook, Robert L., Lawrence O. Jenicke, and Brian Gibson. "Using artificial neural networks for transport decisions: Managerial guidelines." Journal of Transportation Management 21, no. 3 (2010): 18–32. http://dx.doi.org/10.22237/jotm/1285891380.

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One information technology that may be considered by transportation managers, and which is included in the portfolio of technologies that encompass TMS. is artificial neural networks (ANNs). These artificially intelligent computer decision support software provide solutions by finding and recognizing complex patterns in data. ANNs have been used successfully by transportation managers to forecast transportation demand, estimate future transport costs, schedule vehicles and shipments, route vehicles and classify earners for selection. Artificial neural networks excel in transportation decision environments that are dynamic, complex and unstructured. This article introduces ANNs to transport managers by describing ANN technological capabilities, reporting the current status of transportation neural network applications, presenting ANN applications that offer significant potential for future development and offering managerial guidelines for ANN development.
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Dissertations / Theses on the topic "ANN – Artificial Neural Networks"

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Rodríguez, Villegas Antoni. "Polyp segmentation using artificial neural networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-98001.

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Colorectal cancer is the second cause of cancer death in the world. Aiming to early detect and prevent this type of cancer, clinicians perform screenings through the colon searching for polyps (colorectal cancer precursor lesions).If found, these lesions are susceptible of being removed in order to further ana-lyze their malignancy degree. Automatic polyp segmentation is of primary impor-tance when it comes to computer-aided medical diagnosis using images obtained in colonoscopy screenings. These results allow for more precise medical diagnosis which can lead to earlier detection.This project proposed a neural network based solution for semantic segmenta-tion, using the U-net architecture.Combining different data augmentation techniques to alleviate the problem of data scarcity and conducting experiments on the different hyperparameters of the network, the U-net scored a mean Intersection over Union (IoU) of 0,6814. A final approach that combines prediction maps of different models scored a mean IoU of 0,7236.
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Lundin, Johan. "Prediction of Protein Mutations Using Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-400.

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<p>This thesis is concerned with the prediction of protein mutations using artificial neural networks. From the biological perspective it is of interest to investigate weather it is possible to find rules of mutation between evolutionary adjacent (or closely related) proteins. Techniques from computer science are used in order to see if it is possible to predict protein mutations i.e. using artificial neural networks. The computer science perspective of this work would be to try optimizing the results from the neural networks. However, the focus of this thesis is primarily on the biological perspective and the performance of the computer science methods are secondary objective i.e. the primary interest is to show the existence of rules for protein mutations.</p><p>The method used in this thesis consists two neural networks. One network is used to predict the actual protein mutations and the other network is used to make a compressed representation of each amino acid. By using a compression network it is possible to make the prediction network much smaller (each amino acid is represented by 3 nodes instead of 22 nodes). The compression network is an auto associative network and the prediction network is a standard feed-forward network. The prediction network predicts a block of amino acids at a time and for comparison a sliding window technique has also been tested.</p><p>It is my belief that the results in this thesis indicate that there exists rules for protein mutations. However, the tests done in this thesis is only performed on a small portion of all proteins. Some protein families tested show really good results while other families are not as good. I believe that extended work using optimized neural networks would improve the predictions further.</p>
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Ghosh, Ranadhir, and 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.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Information Technology<br>Full Text
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Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics." Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.
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Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics." University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.
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Perchiazzi, Gaetano. "Artificial Neural Networks (ANN) in the Assessment of Respiratory Mechanics." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4665.

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Miranda, Trujillo Luis Carlos. "Artificial Neural Networks in Greenhouse Modelling." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19354.

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Moderne Präzisionsgartenbaulicheproduktion schließt hoch technifizierte Gewächshäuser, deren Einsatz in großem Maße von der Qualität der Sensorik- und Regelungstechnik abhängt, mit ein. Zu den Regelungsstrategien gehören unter anderem Methoden der Künstlichen Intelligenz, wie z.B. Künstliche Neuronale Netze (KNN, aus dem Englischen). Die vorliegende Arbeit befasst sich mit der Eignung KNN-basierter Modelle als Bauelemente von Klimaregelungstrategien in Gewächshäusern. Es werden zwei Modelle vorgestellt: Ein Modell zur kurzzeitigen Voraussage des Gewächshausklimas (Lufttemperatur und relative Feuchtigkeit, in Minuten-Zeiträumen), und Modell zur Einschätzung von phytometrischen Signalen (Blatttemperatur, Transpirationsrate und Photosyntheserate). Eine Datenbank, die drei Kulturjahre umfasste (Kultur: Tomato), wurde zur Modellbildung bzw. -test benutzt. Es wurde festgestellt, dass die ANN-basierte Modelle sehr stark auf die Auswahl der Metaparameter und Netzarchitektur reagieren, und dass sie auch mit derselben Architektur verschiedene Kalkulationsergebnisse liefern können. Nichtsdestotrotz, hat sich diese Art von Modellen als geeignet zur Einschätzung komplexer Pflanzensignalen sowie zur Mikroklimavoraussage erwiesen. Zwei zusätzliche Möglichkeiten zur Erstellung von komplexen Simulationen sind in der Arbeit enthalten, und zwar zur Klimavoraussage in längerer Perioden und zur Voraussage der Photosyntheserate. Die Arbeit kommt zum Ergebnis, dass die Verwendung von KNN-Modellen für neue Gewächshaussteuerungstrategien geeignet ist, da sie robust sind und mit der Systemskomplexität gut zurechtkommen. Allerdings muss beachtet werden, dass Probleme und Schwierigkeiten auftreten können. Diese Arbeit weist auf die Relevanz der Netzarchitektur, die erforderlichen großen Datenmengen zur Modellbildung und Probleme mit verschiedenen Zeitkonstanten im Gewächshaus hin.<br>One facet of the current developments in precision horticulture is the highly technified production under cover. The intensive production in modern greenhouses heavily relies on instrumentation and control techniques to automate many tasks. Among these techniques are control strategies, which can also include some methods developed within the field of Artificial Intelligence. This document presents research on Artificial Neural Networks (ANN), a technique derived from Artificial Intelligence, and aims to shed light on their applicability in greenhouse vegetable production. In particular, this work focuses on the suitability of ANN-based models for greenhouse environmental control. To this end, two models were built: A short-term climate prediction model (air temperature and relative humidity in time scale of minutes), and a model of the plant response to the climate, the latter regarding phytometric measurements of leaf temperature, transpiration rate and photosynthesis rate. A dataset comprising three years of tomato cultivation was used to build and test the models. It was found that this kind of models is very sensitive to the fine-tuning of the metaparameters and that they can produce different results even with the same architecture. Nevertheless, it was shown that ANN are useful to simulate complex biological signals and to estimate future microclimate trends. Furthermore, two connection schemes are proposed to assemble several models in order to generate more complex simulations, like long-term prediction chains and photosynthesis forecasts. It was concluded that ANN could be used in greenhouse automation systems as part of the control strategy, as they are robust and can cope with the complexity of the system. However, a number of problems and difficulties are pointed out, including the importance of the architecture, the need for large datasets to build the models and problems arising from different time constants in the whole greenhouse system.
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Moposita, Tatiana. "Artificial Neural Network (ANN) design using Compute-in-Memory." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS682.

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De nos jours, l'ère du " More than Moore" a émergé comme une influence significative face aux limitations anticipées par la loi de Moore. Les systèmes informatiques explorent des technologies alternatives pour maintenir et améliorer les performances. Cette idée émergé pour résoudre les défis des systèmes électroniques inspirés des réseau biologiques, communément appelés Réseau Neurones Artificiels (ANN). L'utilisation des technologies emerging non-volatile memory (eNVM) est étudiée comme des alternatives prometteuses. Ces technologies offrent plusieurs avantages par rapport à la technologie CMOS traditionnelle, tels qu'une vitesse accrue, des densités plus élevées et une consommation d'énergie moindre. En conséquence, Compute-in-memory utilise les eNVM pour effectuer des calculs directement dans la mémoire, augmentant ainsi la capacité de mémoire et la vitesse de traitement. L'objectif de cette thèse se concentre sur la recherche de la conception de Réseau Neurones Artificiels en utilisant Compute-in-Memory, en employant des solutions matérielles efficaces pour les ANNs tant au niveau du circuit qu'au niveau de l'architecture. Les travaux de recherche récents dans ce contexte ont proposé des conceptions de circuits très efficaces pour optimiser les besoins de calcul énormes nécessaires au traitement des données par les ANNs. Ainsi, pour explorer les capacités d'un ANN au niveau du nœud de sortie, la conception de fonctions d'activation a été proposée. La sélection d'une fonction d'activation est significative car elle détermine la puissance et les capacités du réseau neuronal, et la précision des prédictions dépend principalement de ce choix. Pour évaluer l'efficacité d'une fonction d'activation conçue pour une implémentation analogique, les fonctions d'activation sigmoïde et softmax sont proposées. Cette thèse explore l'intégration de dispositifs mémoires émergents tels que la Spin-Transfer-Torque Magnetic Random Access Memory (STT-MRAM) avec la technologie CMOS. Cette approche combinée vise à tirer parti de la capacité intrinsèque de l'informatique en mémoire offerte par ces dispositifs. Perpendicular magnetic tunneling junction (MTJ) et des FinFET ont été pris en compte pour cette étude. Single-barrier (SMTJ) et double-barrier (DMTJ) sont considérés pour évaluer l'impact de la cellule STT-MRAM basée sur DMTJ par rapport à son homologue SMTJ conventionnel sur les performances d'un réseau neuronal à perceptrons multicouches (MLP) à deux couches. L'évaluation a été réalisée au moyen d'un cadre de simulation personnalisé, de niveaux de dispositif et de cellule jusqu'aux niveaux d'architecture mémoire et d'algorithme. De plus, pour améliorer l'efficacité énergétique d'une architecture Logic-in-Memory (LIM) basée sur les dispositifs STT-MTJ, une nouvelle architecture (SIMPLY+) issue de la logique Smart Material Implication (SIMPLY) et des technologies STT-MRAM basées sur MTJ perpendiculaires a été développée. Le schéma SIMPLY+ constitue une solution prometteuse pour le développement d'architectures informatiques en mémoire économes en énergie et fiables. Toutes les solutions de circuits ont été évaluées à l'aide de simulateurs de circuits commerciaux (par exemple, Cadence Virtuoso). L'activité de conception de circuits impliquant des dispositifs mémoires émergents a également nécessité l'utilisation et le calibrage de modèles compacts basés sur Verilog-A pour intégrer le comportement de ces dispositifs dans l'outil de conception de circuits. Les solutions présentées dans cette thèse impliquent des techniques qui offrent des avancées significatives pour les futures applications. Du point de vue de la conception, l'intégration de modules logiques avec la mémoire STT-MRAM est très réalisable en raison de la compatibilité transparente entre les STT-MRAM et les circuits CMOS. Cette approche est non seulement avantageuse pour la technologie CMOS standard, mais elle exploite également le potentiel des technologies émergentes<br>Nowadays, the era of ”More than Moore” has arisen as a significant influence in light of the limitations anticipated by Moore’s law. The computing systems are exploring alternative technologies to sustain and enhance performance improvements. The idea of alternative innovative technologies has emerged in solving challenges of electronic systems inspired by biological neural networks, commonly referred to as Artificial Neural Network (ANN). The use of emerging non-volatile memory (eNVM) technologies are being explored as promising alternatives. These technologies offer several advantages over traditional CMOS technology, such as increased speed, higher densities, and lower power consumption. As a result, Compute-in-memory employs eNVMs to perform computation within the memory itself, hence increasing memory capacity and processing speed. The objective of this thesis focuses on the research of Artificial Neural Networks design using Compute in Memory, by employing efficient hardware solutions for ANNs at both circuit- and architecture-level. Recent research work in this context has proposed very efficient circuit designs to optimize the enormous computational needs required by data processing by ANNs. Therefore, to explore the capabilities of an ANN at the output node, the design of activation functions were proposed. The selection of an activation function is significant as it determines the power and capabilities of the neural network, and the accuracy of predictions is primarily dependent on this choice. To assess the effectiveness of an activation function designed for analog implementation, the sigmoid and the softmax activation function are proposed. Besides, this thesis explores the integration of emerging memory devices like Spin-Transfer-Torque Magnetic Random Access Memory (STT-MRAM) with CMOS technology. This combined approach aims to leverage the intrinsic capability of in-memory computing offered by these devices. STT-MRAMs based on state-of-the-art perpendicular magnetic tunneling junction (MTJ) and FinFETs has been considered for this study. Single-barrier magnetic tunnel junction (SMTJ) and double-barrier magnetic tunnel junction (DMTJ) devices are considered to evaluate the impact of STT-MRAM cell based on DMTJ against the conventional SMTJ counterpart on the performance of a two-layer multilayer perceptron (MLP) neural network. The assessment was carried out through a customized simulation framework from device and bitcell levels to memory architecture and algorithm levels. Moreover, to improve the energy-efficiency of a Logic-in-Memory (LIM) architecture based on STT-MTJ devices, a new architecture (SIMPLY+) from the Smart Material Implication (SIMPLY) logic and perpendicular MTJ based STT-MRAM technologies was developed. The SIMPLY+ scheme is a promising solution for the development of energy-efficient and reliable in-memory computing architectures. All circuit solutions were evaluated using commercial circuit simulators (e.g. Cadence Virtuoso). Circuit design activity involving emerging memory devices also required the use and calibration of Verilog-A based compact models to integrate the behavior of such devices into the circuit design tool. The solutions presented in this thesis involve techniques that offer significant advancements for future applications. From a design perspective, the integration of logic modules with STT-MRAM memory is highly feasible due to the seamless compatibility between STT-MRAMs and CMOS circuits. This approach not only proves advantageous for standard CMOS technology but also leverages the potential of emerging technologies
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Townsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.

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Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resistant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are interpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbolic Integration [4, 16, 21, 40], in which ANNs are used to produce interpretable, adaptable representations of logic programs and other symbolic models. One neural-symbolic model of reasoning is SHRUTI [89, 95], argued to exhibit biological plausibility in that it captures some aspects of real biological processes. SHRUTI's original developers also suggest that further biological plausibility can be ascribed to the fact that SHRUTI networks can be represented by a model of genetic development [96, 120]. The aims of this thesis are to support the claims of SHRUTI's developers by producing the first such genetic representation for SHRUTI networks and to explore biological plausibility further by investigating the evolvability of the proposed SHRUTI genome. The SHRUTI genome is developed and evolved using principles from Generative and Developmental Systems and Artificial Development [13, 105], in which genomes use indirect encoding to provide a set of instructions for the gradual development of the phenotype just as DNA does for biological organisms. This thesis presents genomes that develop SHRUTI representations of logical relations and episodic facts so that they are able to correctly answer questions on the knowledge they represent. The evolvability of the SHRUTI genomes is limited in that an evolutionary search was able to discover genomes for simple relational structures that did not include conjunction, but could not discover structures that enabled conjunctive relations or episodic facts to be learned. Experiments were performed to understand the SHRUTI fitness landscape and demonstrated that this landscape is unsuitable for navigation using an evolutionary search. Complex SHRUTI structures require that necessary substructures must be discovered in unison and not individually in order to yield a positive change in objective fitness that informs the evolutionary search of their discovery. The requirement for multiple substructures to be in place before fitness can be improved is probably owed to the localist representation of concepts and relations in SHRUTI. Therefore this thesis concludes by making a case for switching to more distributed representations as a possible means of improving evolvability in the future.
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Books on the topic "ANN – Artificial Neural Networks"

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Savacı, F. Acar, ed. Artificial Intelligence and Neural Networks. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11803089.

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Golovko, Vladimir, and Akira Imada, eds. Neural Networks and Artificial Intelligence. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08201-1.

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1949-, Braspenning P. J., Thuijsman F, and Weijters, A. J. M. M., eds. Artificial neural networks: An introduction to ANN theory and practice. Springer, 1995.

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D, Livingstone, ed. Artificial neural networks: Methods and applications. Humana Press, 2008.

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Sharkey, N. E. Artificial intelligence and neural networks group. University of Sheffield, Dept. of Computer Science, 1995.

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Patterson, Dan W. Artificial neural networks: Theory and applications. Prentice Hall, 1996.

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1964-, Mehra Pankaj, and Wah Benjamin W, eds. Artificial neural networks: Concepts and theory. IEEE Computer Society Press, 1992.

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Chiu, Alan Wing Lun. Hybrid neural networks: Using artificial neural networks for the analysis and control of biological neural networks. National Library of Canada, 2002.

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P, Morgan David. Neural Networks and Speech Processing. Springer US, 1991.

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White, Halbert. Artificial neural networks: Approximation and learning theory. Blackwell, 1992.

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Book chapters on the topic "ANN – Artificial Neural Networks"

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Benuskova, Lubica, and Nikola Kasabov. "Artificial Neural Networks (ANN)." In Computational Neurogenetic Modeling. Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-48355-9_4.

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White, Brian, and Mohamed I. Elmasry. "An All-Digital VLSI ANN." In VLSI Artificial Neural Networks Engineering. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2766-4_5.

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Ünal, Muhammet, Ayça Ak, Vedat Topuz, and Hasan Erdal. "Artificial Neural Networks." In Optimization of PID Controllers Using Ant Colony and Genetic Algorithms. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32900-5_2.

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Horner, Heinz, and Reimer Kühn. "Neural Networks." In Intelligence and Artificial Intelligence. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03667-9_8.

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Boreland, Bryson, Herb Kunze, and Kimberly M. Levere. "Artificial Neural Networks." In Engineering Mathematics and Artificial Intelligence. CRC Press, 2023. http://dx.doi.org/10.1201/9781003283980-10.

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Şen, Zekâi. "Artificial Neural Networks." In Shallow and Deep Learning Principles. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29555-3_7.

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Shukla, Anupam, Ritu Tiwari, and Rahul Kala. "Artificial Neural Networks." In Towards Hybrid and Adaptive Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14344-1_2.

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Zhang, Dapeng, Mohamed Kamel, and Mohamed I. Elmasry. "A Parallel ANN Architecture for Fuzzy Clustering." In VLSI Artificial Neural Networks Engineering. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2766-4_8.

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Zhang, Dapeng, Li Deng, and Mohamed I. Elmasry. "A Pipelined ANN Architecture for Speech Recognition." In VLSI Artificial Neural Networks Engineering. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2766-4_9.

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Agbinya, Johnson I. "Artificial Neural Networks." In Applied Data Analytics - Principles and Applications. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337225-8.

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Conference papers on the topic "ANN – Artificial Neural Networks"

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Sutar, Laxmikanta, and Suchismita Chinara. "Smart Healthcare - IoT and Artificial Neural Network(ANN)." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724930.

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Banda-Estrada, A., G. Muñoz-Moreno, J. J. Alfaro-Rodríguez, et al. "Fault Detection in a Multilevel Inverter using Artificial Neural Networks (ANN)." In 2024 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE, 2024. https://doi.org/10.1109/ropec62734.2024.10877140.

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Priyadarsini, Jukta, Veena S. Badiger, Sheetal, and Anitha DSouza Jacintha. "Placement Prediction Using the Artificial Neural Network (ANN)." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915275.

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Bao, Yiyang, Maria Paszkiewicz, Jonas Krimmer, et al. "Loss Prediction and 3D Trajectory Design of Photonic Wire Bonds using Artificial Neural Networks (ANN)." In CLEO: Science and Innovations. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_si.2024.sm1i.3.

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We present an artificial-neural-network-(ANN-)driven concept for predicting transmission losses of 3D-printed freeform waveguides within a few milliseconds and with root-mean-square errors of less than 0.5 %. Our approach enables transmission-optimized trajectory design of photonic wire bonds during fabrication.
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Yang, Zhun, Adam Ishay, and Joohyung Lee. "NeurASP: Embracing Neural Networks into Answer Set Programming." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/243.

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We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neural network in symbolic computation and how it can improve the neural network's perception result by applying symbolic reasoning in answer set programming. Also, NeurASP can make use of ASP rules to train a neural network better so that a neural network not only learns from implicit correlations from the data but also from the explicit complex semantic constraints expressed by the rules.
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Sun, Qiang, John Castagna, and Zhengping Liu. "AVO inversion by Artificial Neural Networks (ANN)." In SEG Technical Program Expanded Abstracts 2000. Society of Exploration Geophysicists, 2000. http://dx.doi.org/10.1190/1.1815637.

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Lai, L. L. "A two-ANN approach to frequency and harmonic evaluation." In Fifth International Conference on Artificial Neural Networks. IEE, 1997. http://dx.doi.org/10.1049/cp:19970734.

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De Vito, S., E. Martinelli, R. Di Fuccio, et al. "Artificial immune systems for Artificial Olfaction data analysis: Comparison between AIRS and ANN models." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596599.

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Khan, Y. U. "Detection of interictal epileptic events in EEG using ANN." In Fifth International Conference on Artificial Neural Networks. IEE, 1997. http://dx.doi.org/10.1049/cp:19970747.

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Zhao, Ningbo, Shuying Li, Zhitao Wang, and Yunpeng Cao. "Prediction of Viscosity of Nanofluids Using Artificial Neural Networks." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-40354.

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The viscosity of nanofluids can be affected by many factors. In pursuit of such improved accuracy, model-based viscosity prediction methods have become more complicated. Therefore, there is a need to find an alternative approach that is able to provide a quick solution to viscosity prediction for nanofluids. In this paper, a novel viscosity prediction approach using artificial neural networks (ANN) is introduced as an alternative to the model-based viscosity prediction approach to provide a quick and accurate estimation of nanofluids viscosity. Radial basis function (RBF) neural networks has been utilized to form viscosity prediction architectures. Alumina (Al2O3)-water nanofluids from existing literatures were used to test the effectiveness of the proposed method. The results showed that RBF neural network model had a reasonable agreement in predicting experimental data. The findings of this paper indicated that the ANN model was an effective method for prediction of the viscosity of nanofluids and had better prediction accuracy and simplicity compared with the other existing theoretical methods.
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Reports on the topic "ANN – Artificial Neural Networks"

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Dawes, Robert L. BIOMASSCOMP: Artificial Neural Networks and Neurocomputers. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada200902.

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 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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Warrick, Arthur W., Gideon Oron, Mary M. Poulton, Rony Wallach, and Alex Furman. Multi-Dimensional Infiltration and Distribution of Water of Different Qualities and Solutes Related Through Artificial Neural Networks. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7695865.bard.

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The project exploits the use of Artificial Neural Networks (ANN) to describe infiltration, water, and solute distribution in the soil during irrigation. It provides a method of simulating water and solute movement in the subsurface which, in principle, is different and has some advantages over the more common approach of numerical modeling of flow and transport equations. The five objectives were (i) Numerically develop a database for the prediction of water and solute distribution for irrigation; (ii) Develop predictive models using ANN; (iii) Develop an experimental (laboratory) database of water distribution with time; within a transparent flow cell by high resolution CCD video camera; (iv) Conduct field studies to provide basic data for developing and testing the ANN; and (v) Investigate the inclusion of water quality [salinity and organic matter (OM)] in an ANN model used for predicting infiltration and subsurface water distribution. A major accomplishment was the successful use of Moment Analysis (MA) to characterize “plumes of water” applied by various types of irrigation (including drip and gravity sources). The general idea is to describe the subsurface water patterns statistically in terms of only a few (often 3) parameters which can then be predicted by the ANN. It was shown that ellipses (in two dimensions) or ellipsoids (in three dimensions) can be depicted about the center of the plume. Any fraction of water added can be related to a ‘‘probability’’ curve relating the size of the ellipse (or ellipsoid) that contains that amount of water. The initial test of an ANN to predict the moments (and hence the water plume) was with numerically generated data for infiltration from surface and subsurface drip line and point sources in three contrasting soils. The underlying dataset consisted of 1,684,500 vectors (5 soils×5 discharge rates×3 initial conditions×1,123 nodes×20 print times) where each vector had eleven elements consisting of initial water content, hydraulic properties of the soil, flow rate, time and space coordinates. The output is an estimate of subsurface water distribution for essentially any soil property, initial condition or flow rate from a drip source. Following the formal development of the ANN, we have prepared a “user-friendly” version in a spreadsheet environment (in “Excel”). The input data are selected from appropriate values and the output is instantaneous resulting in a picture of the resulting water plume. The MA has also proven valuable, on its own merit, in the description of the flow in soil under laboratory conditions for both wettable and repellant soils. This includes non-Darcian flow examples and redistribution and well as infiltration. Field experiments were conducted in different agricultural fields and various water qualities in Israel. The obtained results will be the basis for the further ANN models development. Regions of high repellence were identified primarily under the canopy of various orchard crops, including citrus and persimmons. Also, increasing OM in the applied water lead to greater repellency. Major scientific implications are that the ANN offers an alternative to conventional flow and transport modeling and that MA is a powerful technique for describing the subsurface water distributions for normal (wettable) and repellant soil. Implications of the field measurements point to the special role of OM in affecting wettability, both from the irrigation water and from soil accumulation below canopies. Implications for agriculture are that a modified approach for drip system design should be adopted for open area crops and orchards, and taking into account the OM components both in the soil and in the applied waters.
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Blough, D. K., and K. K. Anderson. A comparison of artificial neural networks and statistical analyses. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10146489.

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Gonzalez Pibernat, Gabriel, and Miguel Mascaró Portells. Dynamic structure of single-layer neural networks. Fundación Avanza, 2023. http://dx.doi.org/10.60096/fundacionavanza/2392022.

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This article examines the practical applications of single hidden layer neural networks in machine learning and artificial intelligence. They have been used in diverse fields, such as finance, medicine, and autonomous vehicles, due to their simplicit
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Waqas, Muhammad Talha. Synaptic Symmetry: Exploring Similarities in Neural Connections between Human Brain and Artificial Neural Networks. ResearchHub Technologies, Inc., 2024. http://dx.doi.org/10.55277/researchhub.c4dckln9.

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Coleman, Andre Michael. An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/971112.

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Markova, Oksana, Serhiy Semerikov та Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network`s weights, etc. The features of the Kolmogorov–Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.
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Rogers, Leah L. Optimal groundwater remediation using artificial neural networks and the genetic algorithm. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10102700.

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Mayfield, Howard T., Delyle Eastwood, and Larry W. Burggraf. Infrared Spectral Classification with Artificial Neural Networks and Classical Pattern Recognition. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada377976.

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