Relevant bibliographies by topics / Artificial Neural Network-based modeling

Academic literature on the topic 'Artificial Neural Network-based modeling'

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Published: 11 March 2023

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Journal articles on the topic "Artificial Neural Network-based modeling"

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Zhang, Ji, Sheng Chang, Hao Wang, Jin He, and Qi Jun Huang. "Artificial Neural Network Based CNTFETs Modeling." Applied Mechanics and Materials 667 (October 2014): 390–95. http://dx.doi.org/10.4028/www.scientific.net/amm.667.390.

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Based on artificial neural network (ANN), a new method of modeling carbon nanotube field effect transistors (CNTFETs) is developed. This paper presents two ANN CNTFET models, including P-type CNTFET (PCNTFET) and N-type CNTFET (NCNTFET). In order to describe the devices more accurately, a segmentation voltage of the voltage between gate and source is defined for each type of CNTFET to segment the workspace of CNTFET. With the smooth connection by a quasi-Fermi function for, the two segmented networks of CNTFET are integrated into a whole device model and implemented by Verilog-A. To validate the ANN CNTFET models, quantitative test with different device intrinsic parameters are done. Furthermore, a complementary CNTFET inverter is designed using these NCNTFET and PCNTFET ANN models. The performances of the inverter show that our models are both efficient and accurate for simulation of nanometer scale circuits.
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Hiyama, T., M. Tokieda, W. Hubbi, and H. Andou. "Artificial neural network based dynamic load modeling." IEEE Transactions on Power Systems 12, no. 4 (1997): 1576–83. http://dx.doi.org/10.1109/59.627861.

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Wang, Jun, Feng Qin Yu, and Feng He Wu. "Cutting Data Modeling Based on Artificial Neural Network." Key Engineering Materials 620 (August 2014): 544–49. http://dx.doi.org/10.4028/www.scientific.net/kem.620.544.

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Cutting force is usually obtained based on the experimental data which is conducted under certain cutting condition with certain cutters because metal cutting mechanism study is not mature. As the data are numerous, in different types, and the relationships between them are complex, the commercial database can be used directly. A new approach based on ANN is introduced here for unstructured and discrete data modeling, which transfers the unstructured and discrete data into ANN topology and net weight matrix. In this paper, the experimental data of union cutting force modification are taken as examples for verifying the feasibility of the ANN model. The ANN modeling, inputs, outputs and ANN training are discussed. Compared with other modeling approaches, this model is general and can process discrete data with unified data structure. This model can be used for cutting force calculation as well as intelligent and general CAPP.
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Faghri, Ardeshir, and Sandeep Aneja. "Artificial Neural Network–Based Approach to Modeling Trip Production." Transportation Research Record: Journal of the Transportation Research Board 1556, no. 1 (January 1996): 131–36. http://dx.doi.org/10.1177/0361198196155600115.

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Accurate and reliable estimates of trip production of a study area are important for an accurate forecast from the four-step travel demand forecasting procedure. In the trip generation step, trip production estimates are considered more accurate, and trip attractions are adjusted while keeping the productions constant. This means that more accurate trip production rates will result in more reliable forecasts. Improving the accuracy of forecasts requires an extensive and reliable data base or improvement in the modeling techniques. Since data base enhancement is costly and time-consuming, an alternative methodology is proposed and examined for trip production prediction using artificial neural network (ANN) concepts and techniques. The data base used was made available by the Delaware Department of Transportation. The data were collected for 60 sites throughout Delaware between 1970 and 1974 and are based on field counts and home interviews. Twenty-six regression models were calibrated on these data. In addition, 18 ANN architectures were developed, and their predictions were compared with those from regression models. Comparisons indicate that the ANNs have the capability to represent the relationship between the trip production rate and the independent variables more accurately than regression analysis at no additional cost of increasing the data base.
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Longfei, Tang, Xu Zhihong, and Bala Venkatesh. "Contactor Modeling Technology Based on an Artificial Neural Network." IEEE Transactions on Magnetics 54, no. 2 (February 2018): 1–8. http://dx.doi.org/10.1109/tmag.2017.2767555.

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Panahi, Shirin, Zainab Aram, Sajad Jafari, Jun Ma, and J. C. Sprott. "Modeling of epilepsy based on chaotic artificial neural network." Chaos, Solitons & Fractals 105 (December 2017): 150–56. http://dx.doi.org/10.1016/j.chaos.2017.10.028.

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Rai, Raveendra K., and B. S. Mathur. "Event-based Sediment Yield Modeling using Artificial Neural Network." Water Resources Management 22, no. 4 (May 4, 2007): 423–41. http://dx.doi.org/10.1007/s11269-007-9170-3.

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Xie, Shuai, Wenyan Wu, Sebastian Mooser, Q. J. Wang, Rory Nathan, and Yuefei Huang. "Artificial neural network based hybrid modeling approach for flood inundation modeling." Journal of Hydrology 592 (January 2021): 125605. http://dx.doi.org/10.1016/j.jhydrol.2020.125605.

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HASEENA, H., PAUL K. JOSEPH, and ABRAHAM T. MATHEW. "ARTIFICIAL NEURAL NETWORK BASED ECG ARRHYTHMIA CLASSIFICATION." Journal of Mechanics in Medicine and Biology 09, no. 04 (December 2009): 507–25. http://dx.doi.org/10.1142/s0219519409003103.

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Reliable and computationally efficient means of classifying electrocardiogram (ECG) signals has been the subject of considerable research effort in recent years. This paper explores the potential applications of a talented, versatile computation model called the Artificial Neural Network (ANN) in the field of ECG signal classification. Two types of ANNs: Multi-Layered Feed Forward Network (MLFFN) and Probabilistic Neural Networks (PNN) are used to classify seven types of ECG beats. It includes six types of arrhythmia data and normal data. Here, parametric modeling strategies are used in conjunction with ANN classifiers to discriminate ECG signals. Instead of giving the ECG data as such, parameters such as fourth order Auto Regressive model coefficients and Spectral Entropy of the signals has been selected. On testing with the Massachusetts Institute of Technology-Beth Israel Hospital (MIT/BIH) arrhythmia database, it has been observed that PNN has better performance than conventionally used MLFFN in ECG arrhythmia classification. MLFFN with Back Propagation Algorithm gives a classification accuracy of 97.54% and PNN gives 98.96%. The classification by PNN also has an advantage that the computation time for classification is lower than that of MLFFN.
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Çelik, Şenol. "MODELING AVOCADO PRODUCTION IN MEXICO WITH ARTIFICIAL NEURAL NETWORKS." Engineering and Technology Journal 07, no. 10 (October 31, 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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Dissertations / Theses on the topic "Artificial Neural Network-based modeling"

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Brunger, Clifford A. "Artificial neural network modeling of damaged aircraft." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA283227.

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Saptoro, Agus. "An integrated approach to artificial neural network based process modelling." Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/2484.

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ANN technology exploded into the world of process modelling and control in the late 1980’s. The technology shows great promise and is seen as a technology that could provide models for most systems without the need to understand the fundamental behaviour or relationships among the process variables. Today, ANN applications have been applied successfully in a number of areas of process modelling and control, with the best-established applications being in the area of inferential measurements or soft sensors.Unfortunately, ‘the free lunch did not have much meat’. Overtime, people focused more on the true capabilities and power of ANN, the ability to model nonlinear relationships in data without having to define the form of the nonlinearity. However, there is often a tendency to merely plug in the data, turn the ANN training software on, and blindly accept the results. This is probably inevitable since, to date, there are no textbooks or scientific journal papers providing an integrated and systematic approach for ANN model development addressing pre-modelling, training and postmodelling stages. Therefore, addressing issues in those three phases of ANN model development is essential to support and to improve further applications of ANN technology in the area of process modelling and control.The model development issues in pre-modelling and training phases were addressed by reviewing current practice and existing techniques. For each issue, a novel method was proposed to improve the performance of ANN models. The new approaches were tested in a variety of benchmarking studies using artificial samples and coal property datasets from power station boilers.The research work in the post-modelling stage analysis which emphasises on taking the lid off black box model, proposes a novel technique to extract knowledge from the models and simultaneously obtain better understanding of the process. Postmodelling phase issues were addressed thoroughly including construction of prediction limit, sensitivity analysis and development of mathematical representation of the trained ANN model.Confidence intervals of the ANN models were analysed to construct the prediction boundary of the model. This analysis provides useful information related to interpolation and extrapolation of the model. It also highlighted how good the ANN models can be used for extrapolation purposes.An effort based on sensitivity analysis of hidden layers is also proposed to understand the behaviours of the ANN models. Using this technique, knowledge and information are retrieved from the developed models. A comparative study of the proposed techniques and the current practice was also presented.The last topic addressed in this thesis is knowledge extraction of ANN models using mathematical analysis of the hidden layers. The proposed analysis is applied in order to open the black box of the ANN models and is implemented to simulated and real historical plant data so that useful information from those data and better understanding of the process are obtained.All in all, efforts have been made in this thesis to minimise the use of abstract mathematical language and in some cases, simplify the language so that ANN modelling theory can be understood by a wider range of audience, especially the new practitioners in ANN based modelling and control. It is hoped that the insight provided in the dissertation will provide an integrated approach to pre-modelling, training and post-modelling stages of ANN models. This ‘new guideline’ of ANN model development is unique and beneficial, providing a systematic framework for the preparation, design, evaluation and implementation of ANN models in process modelling and control in particular and prediction / forecasting tool in general.
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Ajayi, Toluwaleke. "Modeling Discharge and Water Chemistry Using Artificial Neural Network." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1620167556121952.

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Rothrock, Ling. "Modeling skilled decision-making using artificial neural network and genetic-based machine learning techniques." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/25084.

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Byrne, Brian James. "An evaluation of artificial neural network modeling for manpower analysis." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA273001.

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Thesis (M.S. in Management) Naval Postgraduate School, September 1993.
Thesis advisor(s): George W. Thomas ; Timothy P. Hill. "September 1993." Includes bibliographical references. Also available online,
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FALCIONELLI, NICOLA. "From Symbolic Artificial Intelligence to Neural Networks Universality with Event-based Modeling." Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/274620.

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Rappresentare la conoscenza, modellare il ragionamento umano e comprendere i processi di pensiero sono sempre state parti centrali delle attività intellettuali, fin dai primi tentativi dei filosofi greci. Non è solo un caso che, non appena i computer hanno iniziato a diffondersi, scienziati e matematici straordinari come John McCarthy, Marvin Minsky e Claude Shannon hanno iniziato a creare sistemi Artificialmente Intelligenti con una prospettiva orientata al simbolismo. Anche se questo è stato un percorso parzialmente forzato a causa delle capacità di calcolo molto limitate dell'epoca, ha segnato l'inizio di quella che oggi è conosciuta come Intelligenza Artificiale Classica (o Simbolica), o essenzialmente, un insieme di tecniche per implementare comportamenti "intelligenti" attraverso formalismi logici e di dimostrazione di teoremi. Le tecniche di Intelligenza Artificiale Classica sono infatti processi molto diretti e centrati sull'uomo, che trovano il loro punto di forza nella semplice interpretabilità umana e nella riusabilità della conoscenza. Al contrario, esse soffrono di problemi di computabilità quando sono applicate a compiti del mondo reale, per lo più dovuti all'esplosione combinatoria dello spazio di ricerca (soprattutto quando si ha a che fare con il tempo), e all'indecidibilità. Tuttavia, le sempre maggiori capacità dell'hardware dei computer hanno aperto nuove possibilità di crescita per altri metodi più orientati alla statistica, come le Reti Neurali. Anche se la teoria alla base di questi metodi era nota da tempo, è stato solo negli ultimi anni che sono riusciti a raggiungere progressi significativi, e a superare le tecniche classiche di IA su molti fronti. Al momento, i principali ostacoli di tali tecniche di IA statistica sono rappresentati dall'elevato consumo di energia e dalla mancanza di modi semplici per gli esseri umani di comprendere il processo che ha portato a un particolare risultato. Riassumendo, le tecniche di IA classica e statistica possono essere viste come due facce della stessa medaglia: se un dominio presenta informazioni strutturate, poca incertezza e processi decisionali chiari, allora l'IA classica potrebbe essere lo strumento giusto, o altrimenti, quando le informazioni sono meno strutturate, hanno più incertezza, ambiguità e non è possibile identificare processi decisionali chiari, allora l'IA statistica dovrebbe essere scelta. Lo scopo principale di questa tesi è quindi (i) mostrare le capacità e i limiti delle attuali tecniche di Intelligenza Artificiale (Classica e Statistica) sia in ambiti strutturati che non strutturati, e (ii) dimostrare come la modellazione basata su eventi possa affrontare alcune delle loro criticità, fornendo nuove potenziali connessioni e nuove prospettive.
Representing knowledge, modeling human reasoning, and understanding thought processes have always been central parts of intellectual activities, since the first attempts by greek philosophers. It is not just by chance that, as soon as computers started to spread, remarkable scientists and mathematicians such as John McCarthy, Marvin Minsky and Claude Shannon started creating Artificially Intelligent systems with a symbolic oriented perspective. Even though this has been a partially forced path due to the very limited computing capabilities at the time, it marked the beginning of what is now known as Classical (or Symbolic) Artificial Intelligence, or essentially, a set of techniques for implementing "intelligent" behaviours by means of logic formalisms and theorem proving. Classical AI techniques are indeed very direct and human-centered processes, which find their strenghts on straightforward human interpretability and knowledge reusability. On the contrary, they suffer of computability problems when applied to real world tasks, mostly due to search space combinatorial explosion (especially when reasoning with time), and undecidability. However, the ever-increasing capabilites of computer hardware opened new possibilities for other more statistical-oriented methods to grow, such as Neural Networks. Even if the theory behind these methods was long known, it was only in recent years that they managed to achieve significant breakthroughs, and to surpass Classical AI techniques on many tasks. At the moment, the main hurdles of such statistical AI techniques are represented by the high energy consumption and the lack of easy ways for humans to understand the process that led to a particular result. Summing up, Classical and Statistical AI techniques can be seen as two faces of the same coin: if a domain presents structured information, little uncertainty, and clear decision processes, then Classical AI might be the right tool, or otherwise, when the information is less structured, has more uncertainty, ambiguity and clear decision processes cannot be identified, then Statistical AI should be chosen. The main purpose of this thesis is thus (i) to show capabilities and limits of current (Classical and Statistical) Artificial Intelligence techniques in both structured and unstructured domains, and (ii) to demostrate how event-based modeling can tackle some of their critical issues, providing new potential connections and novel perspectives.
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FLECK, JULIA LIMA. "ARTIFICIAL NEURAL NETWORK MODELING FOR QUALITY INFERENCE OF A POLYMERIZATION PROCESS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=12980@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
O presente trabalho apresenta o desenvolvimento de um modelo neural para a inferência da qualidade do polietileno de baixa densidade (PEBD) a partir dos valores das variáveis de processo do sistema reacional. Para tal, fez- se uso de dados operacionais de uma empresa petroquímica, cujo pré-processamento incluiu a seleção de variáveis, limpeza e normalização dos dados selecionados e preparação dos padrões. A capacidade de inferência do modelo neural desenvolvido neste estudo foi comparada com a de dois modelos fenomenológicos existentes. Para tal, utilizou-se como medida de desempenho o valor do erro médio absoluto percentual dos modelos, tendo como referência valores experimentais do índice de fluidez. Neste contexto, o modelo neural apresentou-se como uma eficiente ferramenta de modelagem da qualidade do sistema reacional de produção do PEBD.
This work comprises the development of a neural network- based model for quality inference of low density polyethylene (LDPE). Plant data corresponding to the process variables of a petrochemical company`s LDPE reactor were used for model development. The data were preprocessed in the following manner: first, the most relevant process variables were selected, then data were conditioned and normalized. The neural network- based model was able to accurately predict the value of the polymer melt index as a function of the process variables. This model`s performance was compared with that of two mechanistic models developed from first principles. The comparison was made through the models` mean absolute percentage error, which was calculated with respect to experimental values of the melt index. The results obtained confirm the neural network model`s ability to infer values of quality-related measurements of the LDPE reactor.
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Li, Tan. "Tire-Pavement Interaction Noise (TPIN) Modeling Using Artificial Neural Network (ANN)." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/87417.

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Tire-pavement interaction is a dominant noise source for passenger cars and trucks above 25 mph (40 km/h) and 43 mph (70 km/h), respectively. For the same pavement, tires with different tread pattern and construction generate noise of different levels and frequencies. In the present study, forty-two different tires were tested over a range of speeds (45-65 mph, i.e., 72-105 km/h) on a non-porous asphalt pavement (a section of U.S. Route 460, both eastbound and westbound). An On-Board Sound Intensity (OBSI) system was instrumented on the test vehicle to collect the tire noise data at both the leading and trailing edge of the tire contact patch. An optical sensor recording the once-per-revolution signal of the wheel was also installed to monitor the vehicle speed and, more importantly, to provide the data needed to perform the order tracking analysis in order to break down the tire noise into two components. These two components are: the tread pattern and the non-tread pattern noise. Based on the experimental noise data collected, two artificial neural networks (ANN) were developed to predict the tread pattern (ANN1) and the non-tread pattern noise (ANN2) components, separately. The inputs of ANN1 are the coherent tread profile spectrum and the air volume velocity spectrum calculated from the digitized 3D tread pattern. The inputs of ANN2 are the tire size and tread rubber hardness. The vehicle speed is also included as input for the two ANN's. The optimized ANN's are able to predict the tire-pavement interaction noise well for different tires on the pavement tested. Another outcome of this work is the complete literature review on Tire-Pavement Interaction Noise (TPIN), as an appendix of this dissertation and covering ~1000 references, which might be the most comprehensive compilation of this topic.
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Bhanot, Nishant. "Artificial Neural Networks based Modeling and Analysis of Semi-Active Damper System." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78295.

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The suspension system is one of the most sensitive systems of a vehicle as it affects the dynamic behavior of the vehicle with even minor changes. These systems are designed to carry out multiple tasks such as isolating the vehicle body from the road/tire vibrations as well as achieving desired ride and handling performance levels in both steady state and limit handling conditions. The damping coefficient of the damper plays a crucial role in determining the overall frequency response of the suspension system. Considerable research has been carried out on semi active damper systems as the damping coefficient can be varied without the system requiring significant external power giving them advantages over both passive and fully active suspension systems. Dampers behave as non-linear systems at higher frequencies and hence it has been difficult to develop accurate models for its full range of motion. This study aims to develop a velocity sensitive damper model using artificial neural networks and essentially provide a 'black-box' model which encapsulates the non-linear behavior of the damper. A feed-forward neural network was developed by testing a semi active damper on a shock dynamometer at CenTiRe for multiple frequencies and damping ratios. This data was used for supervised training of the network using MATLAB Neural Network Toolbox. The developed NN model was evaluated for its prediction accuracy. Further, the developed damper model was analyzed for feasibility of use for simulations and controls by integrating it in a Simulink based quarter car model and applying the well-known skyhook control strategy. Finally, effects on ride and handling dynamics were evaluated in Carsim by replacing the default damper model with the proposed model. It was established that this damper modeling technique can be used to help evaluate the behavior of the damper on both component as well as vehicle level without needing to develop a complex physics based model. This can be especially beneficial in the earlier stages of vehicle development.
Master of Science
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Kvale, David Thomas. "Artificial Neural Network-Based Approaches for Modeling the Radiated Emissions from Printed Circuit Board Structures and Shields." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1280698960.

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Books on the topic "Artificial Neural Network-based modeling"

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Artificial neural network modeling of water and wastewater treatment processes. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Khataee, A. R. Artificial neural network modeling of water and wastewater treatment processes. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Guan, Biing T. Modeling training site vegetation coverage probability with a random optimization procedure: An artificial neural network approach. [Champaign, IL]: US Army Corps of Engineers, Construction Engineering Research Laboratories, 1998.

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Shanmuganathan, Subana, and Sandhya Samarasinghe, eds. Artificial Neural Network Modelling. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8.

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S, Mohan. Artificial neural network modelling. Roorkee: Indian National Committee on Hydrology, 2007.

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National Hydrology Symposium (4th 1993 Cardiff, Wales). Rainfall-runoff modeling as a problem in artificial intelligence: experience with a neural network. Fourth National Hydrology Symposium: (held at) University of Wales College of Cardiff 13-16th September 1993. (London?): British Hydrological Society, 1993.

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Thrun, Sebastian. Explanation-Based Neural Network Learning: A Lifelong Learning Approach. Boston, MA: Springer US, 1996.

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Thrun, Sebastian. Explanation-based neural network learning: A lifelong learning approach. Boston: Kluwer Academic Publishers, 1996.

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White, Roger. The artificial intelligence of urban dynamics: Neural network modelling of urban structure. [Toronto]: Centre for Urban and Community Studies, University of Toronto, 1989.

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Daniel, Sarit. Wavelet based artificial neural network and entropy detection techniques for a chaosmaker. Ottawa: National Library of Canada, 2002.

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Book chapters on the topic "Artificial Neural Network-based modeling"

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Božnar, Marija Zlata, and Primož Mlakar. "Artificial Neural Network-Based Environmental Models." In Air Pollution Modeling and Its Application XIV, 483–92. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/0-306-47460-3_49.

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Belkhode, Pramod, Sarika Modak, Vinod Ganvir, and Anand Shende. "Artificial Neural Network Simulation." In Mathematical Modeling and Simulation, 63–85. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003132127-7.

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Rao, Ming, Qijun Xia, and Yiqun Ying. "Modeling via Artificial Neural Network." In Modeling and Advanced Control for Process Industries, 245–63. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2094-0_9.

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Xiong, Wangping, Jianqiang Du, Qinglong Shu, and Yi Zhao. "Artificial Neural Network Based Modeling of Glucose Metabolism." In Advances in Computer Science, Intelligent System and Environment, 623–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23756-0_100.

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Bataineh, Mohammad, Timothy Marler, and Karim Abdel-Malek. "Artificial Neural Network-Based Prediction of Human Posture." In Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management. Human Body Modeling and Ergonomics, 305–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39182-8_36.

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Aziz, Kashif, Ataur Rahman, and Asaad Shamseldin. "Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia." In Artificial Neural Network Modelling, 307–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_13.

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Septiawan, Reza, Arief Rufiyanto, Sardjono Trihatmo, Budi Sulistya, Erik Madyo Putro, and Subana Shanmuganathan. "Artificial Neural Network (ANN) Pricing Model for Natural Rubber Products Based on Climate Dependencies." In Artificial Neural Network Modelling, 423–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_20.

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Al-Yousef, Ali, and Sandhya Samarasinghe. "Improved Ultrasound Based Computer Aided Diagnosis System for Breast Cancer Incorporating a New Feature of Mass Central Regularity Degree (CRD)." In Artificial Neural Network Modelling, 213–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_10.

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Lemetre, Christophe, Lee J. Lancashire, Robert C. Rees, and Graham R. Ball. "Artificial Neural Network Based Algorithm for Biomolecular Interactions Modeling." In Lecture Notes in Computer Science, 877–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02478-8_110.

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Dutta, Pijush, Souvik Pal, Asok Kumar, and Korhan Cengiz. "A Practical Approach to Neural Network Models." In Artificial Intelligence for Cognitive Modeling, 43–72. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003216001-4.

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Conference papers on the topic "Artificial Neural Network-based modeling"

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Sousa, Miguel Angelo de Abreu de, and Thiago Felipe de Jesus Torres. "Modeling of Pain on a FPGA-based Neural Network." In Artificial Intelligence and Applications. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.793-034.

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Karacor, Mevlut, Kadir Yilmaz, and Feriha Erfan Kuyumcu. "Modeling MCSRM with artificial neural network." In 2007 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) and Electromotion '07. IEEE, 2007. http://dx.doi.org/10.1109/acemp.2007.4510569.

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Hiyama, T., N. Suzuki, H. Karino, Kwang Yun Lee, and H. Andou. "Artificial neural network based modeling of governor-turbine system." In IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233). IEEE, 1999. http://dx.doi.org/10.1109/pesw.1999.747437.

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A . K., Prakash, Amol Patil, and Kalyani U. "Artificial Neural Network Based Driver Modeling for Vehicle Systems." In 8th SAEINDIA International Mobility Conference & Exposition and Commercial Vehicle Engineering Congress 2013 (SIMCOMVEC). 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2013. http://dx.doi.org/10.4271/2013-01-2860.

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Huang, Jiarong, Guangqin Gao, Xianyu Meng, and Yuxiu Guan. "Modeling stand density index based on artificial neural network." In 2010 Sixth International Conference on Natural Computation (ICNC). IEEE, 2010. http://dx.doi.org/10.1109/icnc.2010.5584350.

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Gupta, Subham, Achyut Paudel, Mishal Thapa, Sameer B. Mulani, and Robert Walters. "Adaptive Sampling-Based Artificial Neural Network for Surrogate Modeling." In AIAA SCITECH 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-0805.

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Yurshin, V. G. "Artificial Neural Network Architecture Tuning Algorithm." In International Workshop “Hybrid methods of modeling and optimization in complex systems”. European Publisher, 2023. http://dx.doi.org/10.15405/epct.23021.29.

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Wang, Yongqing, Huawei Shen, Shenghua Liu, Jinhua Gao, and Xueqi Cheng. "Cascade Dynamics Modeling with Attention-based Recurrent Neural Network." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/416.

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An ability of modeling and predicting the cascades of resharing is crucial to understanding information propagation and to launching campaign of viral marketing. Conventional methods for cascade prediction heavily depend on the hypothesis of diffusion models, e.g., independent cascade model and linear threshold model. Recently, researchers attempt to circumvent the problem of cascade prediction using sequential models (e.g., recurrent neural network, namely RNN) that do not require knowing the underlying diffusion model. Existing sequential models employ a chain structure to capture the memory effect. However, for cascade prediction, each cascade generally corresponds to a diffusion tree, causing cross-dependence in cascade---one sharing behavior could be triggered by its non-immediate predecessor in the memory chain. In this paper, we propose to an attention-based RNN to capture the cross-dependence in cascade. Furthermore, we introduce a \emph{coverage} strategy to combat the misallocation of attention caused by the memoryless of traditional attention mechanism. Extensive experiments on both synthetic and real world datasets demonstrate the proposed models outperform state-of-the-art models at both cascade prediction and inferring diffusion tree.
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Thoeurn, Muy, Ardyono Priyadi, Anang Tjahjono, and Mauridhi Hery Purnomo. "Overcurrent relay modeling using artificial neural network." In 2017 International Electrical Engineering Congress (iEECON). IEEE, 2017. http://dx.doi.org/10.1109/ieecon.2017.8075794.

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Sapounaki, Maria, and Athanasios Kakarountas. "A High-Performance Neuron for Artificial Neural Network based on Izhikevich model." In 2019 29th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS). IEEE, 2019. http://dx.doi.org/10.1109/patmos.2019.8862154.

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Reports on the topic "Artificial Neural Network-based modeling"

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Hsieh, Bernard B., and Charles L. Bartos. Riverflow/River Stage Prediction for Military Applications Using Artificial Neural Network Modeling. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada382991.

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Markova, Oksana, Serhiy Semerikov, and Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, May 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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Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3683.

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The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used research methods such as analysis of the literature on banking scoring; artificial intelligence methods for scoring; modeling of scoring estimation algorithm using neural networks, empirical method for determining the optimal parameters of the training model; method of object-oriented design and programming. The result of the work is a neural network scoring model with high accuracy of calculations, an implemented system of automatic customer lending.
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Fix, Edward L. Neural Network Based Human Performance Modeling. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada229822.

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Bhatikar, S. R., R. L. Mahajan, K. Wipke, and V. Johnson. Neural Network Based Energy Storage System Modeling for Hybrid Electric Vehicles. Office of Scientific and Technical Information (OSTI), August 1999. http://dx.doi.org/10.2172/935117.

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Al-Qadi, Imad, Jaime Hernandez, Angeli Jayme, Mojtaba Ziyadi, Erman Gungor, Seunggu Kang, John Harvey, et al. The Impact of Wide-Base Tires on Pavement—A National Study. Illinois Center for Transportation, October 2021. http://dx.doi.org/10.36501/0197-9191/21-035.

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Researchers have been studying wide-base tires for over two decades, but no evidence has been provided regarding the net benefit of this tire technology. In this study, a comprehensive approach is used to compare new-generation wide-base tires (NG-WBT) with the dual-tire assembly (DTA). Numerical modeling, prediction methods, experimental measurements, and environmental impact assessment were combined to provide recommendations about the use of NG-WBT. A finite element approach, considering variables usually omitted in the conventional analysis of flexible pavement was utilized for modeling. Five hundred seventy-six cases combining layer thickness, material properties, tire load, tire inflation pressure, and pavement type (thick and thin) were analyzed to obtained critical pavement responses. A prediction tool, known as ICT-Wide, was developed based on artificial neural networks to obtain critical pavement responses in cases outside the finite element analysis matrix. The environmental impacts were determined using life cycle assessment. Based on the bottom-up fatigue cracking, permanent deformation, and international roughness index, the life cycle energy consumption, cost, and green-house gas (GHG) emissions were estimated. To make the outcome of this research effort useful for state departments of transportation and practitioners, a modification to AASHTOWare is proposed to account for NG-WBT. The revision is based on two adjustment factors, one accounting for the discrepancy between the AASHTOware approach and the finite element model of this study, and the other addressing the impact of NG-WBT.
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Semerikov, Serhiy, Hanna Kucherova, and Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (Covid-19). Stylos, December 2020. http://dx.doi.org/10.31812/123456789/4133.

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The paper proposes an approach to modeling the business climate of the country, which is based on the principles of information transparency, and makes it possible to assess the development trends of the studied indicator in conditions of the COVID-19. This approach has been tested on the example of Ukraine. The results obtained make it possible to analyze the cyclical development of the country's economy with high accuracy and reliability even under quarantine restrictions.
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Semerikov, Serhiy, Hanna Kucherova, Vita Los, and Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19). CEUR Workshop Proceedings, April 2021. http://dx.doi.org/10.31812//123456789/4364.

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The prospects for doing business in countries are also determined by the business confidence index. The purpose of the article is to model trends in indicators that determine the state of the business climate of countries, in particular, the period of influence of the consequences of COVID-19 is of scientific interest. The approach is based on the preliminary results of substantiating a set of indicators and applying the taxonomy method to substantiate an alternative indicator of the business climate, the advantage of which is its advanced nature. The most significant factors influencing the business climate index were identified, in particular, the annual GDP growth rate and the volume of retail sales. The similarity of the trends in the calculated and actual business climate index was obtained, the forecast values were calculated with an accuracy of 89.38%. And also, the obtained modeling results were developed by means of building and using neural networks with learning capabilities, which makes it possible to improve the quality and accuracy of the business climate index forecast up to 96.22%. It has been established that the consequences of the impact of COVID-19 are forecasting a decrease in the level of the country's business climate index in the 3rd quarter of 2020. The proposed approach to modeling the country's business climate is unified, easily applied to the macroeconomic data of various countries, demonstrates a high level of accuracy and quality of forecasting. The prospects for further research are modeling the business climate of the countries of the world in order to compare trends and levels, as well as their changes under the influence of quarantine restrictions.
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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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