To see the other types of publications on this topic, follow the link: ANN – Artificial Neural Networks.
Journal articles on the topic 'ANN – Artificial Neural Networks'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'ANN – Artificial Neural Networks.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
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.
Full textAbstract:
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.
2
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.
Full textAbstract:
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.
3
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.
Full textAbstract:
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.
4
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.
Full textAbstract:
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.
5
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.
Full textAbstract:
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.
6
Doroshenko, Anna. "Applying Artificial Neural Networks In Construction." E3S Web of Conferences 143 (2020): 01029. http://dx.doi.org/10.1051/e3sconf/202014301029.
Full textAbstract:
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.
7
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.
Full textAbstract:
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.
8
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.
Full text
9
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.
Full textAbstract:
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.
10
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.
Full textAbstract:
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.
11
Bachir, Naas, Benalia M’hamdi, Amari Abderrahmane, and Naas Badreddine. "Double star permanent magnet synchronous machine: modified direct torque control." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 1 (2024): 974–87. http://dx.doi.org/10.54021/seesv5n1-051.
Full textAbstract:
In the area of high power drives, double star synchronous machines are an interesting choice compared to conventional synchronous machines, due to the relatively low torque ripple created. In this paper, direct torque control (DTC) of double star permanent magnet synchronous machine (DS-PMSM) using artificial neural networks (ANN) is proposed. MATLAB/Simulink results show the comparison between direct torque control (DTC) and direct torque control using artificial neural networks (ANN). The analysis of the results shows good performance for speed, small torque and flow ripple when using the artificial neural network (ANN) strategy.
12
Lu, P. J., M. C. Zhang, T. C. Hsu, and J. Zhang. "An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks." Journal of Engineering for Gas Turbines and Power 123, no. 2 (2001): 340–46. http://dx.doi.org/10.1115/1.1362667.
Full textAbstract:
Application of artificial neural network (ANN)-based method to perform engine condition monitoring and fault diagnosis is evaluated. Back-propagation, feedforward neural nets are employed for constructing engine diagnostic networks. Noise-contained training and testing data are generated using an influence coefficient matrix and the data scatters. The results indicate that under high-level noise conditions ANN fault diagnosis can only achieve a 50–60 percent success rate. For situations where sensor scatters are comparable to those of the normal engine operation, the success rates for both four-input and eight-input ANN diagnoses achieve high scores which satisfy the minimum 90 percent requirement. It is surprising to find that the success rate of the four-input diagnosis is almost as good as that of the eight-input. Although the ANN-based method possesses certain capability in resisting the influence of input noise, it is found that a preprocessor that can perform sensor data validation is of paramount importance. Autoassociative neural network (AANN) is introduced to reduce the noise level contained. It is shown that the noise can be greatly filtered to result in a higher success rate of diagnosis. This AANN data validation preprocessor can also serve as an instant trend detector which greatly improves the current smoothing methods in trend detection. It is concluded that ANN-based fault diagnostic method is of great potential for future use. However, further investigations using actual engine data have to be done to validate the present findings.
13
Michaud, François, and Ruben Gonzalez Rubio. "Autonomous Design of Artificial Neural Networks by Neurex." Neural Computation 8, no. 8 (1996): 1767–86. http://dx.doi.org/10.1162/neco.1996.8.8.1767.
Full textAbstract:
Artificial neural networks (ANN) have been demonstrated to be increasingly more useful for complex problems difficult to solve with conventional methods. With their learning abilities, they avoid having to develop a mathematical model or acquiring the appropriate knowledge to solve a task. The difficulty now lies in the ANN design process. A lot of choices must be made to design an ANN, and there are no available design rules to make these choices directly for a particular problem. Therefore, the design of an ANN demands a certain number of iterations, mainly guided by the expertise and the intuition of the developer. To automate the ANN design process, we have developed Neurex, composed of an expert system and an ANN simulator. Neurex autonomously guides the iterative ANN design process. Its structure tries to reproduce the design steps done by a human expert in conceiving an ANN. As a whole, the Neurex structure serves as a framework to implement this expertise for different learning paradigms. This article presents the system's general characteristics and its use in designing ANN using the standard backpropagation learning law.
14
Salim, Hosham, Khalid Faisal, and Raheel Jawad. "Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design." Applied Computational Intelligence and Soft Computing 2018 (December 2, 2018): 1–9. http://dx.doi.org/10.1155/2018/8042498.
Full textAbstract:
Design and implantation of electric circuit for enhanced performance of steam power plant and artificial neural networks technique are used to control turbine. Artificial neural networks technique is used to control a lot of industrial models practically. Artificial neural network has been applied to control the important variables of turbine in AL–Dura power plant in Baghdad such as pressure, temperature, speed, and humidity. In this study Simulink model was applied in MATLAB program (v 2014 a) by using artificial neural network (ANN). The method of controlling model is by using NARMA to generate data and train network. ANN is offline. ANN requires data to obtain results and for comparison with actual power plant. The values of the input variables have a large effect on the number of nodes and epochs and in hidden layer of the artificial neural network they also affect performance of ANN. The electric circuit of sensors consists of transformer, DC bridge, and voltage regulator. Comparing the results from modeling by ANN and electric circuit with experimental data reveals a good agreement and the maximum deviation between the experimental data and predicted results from ANN and circuit design is less than 1%. The novelty in this paper is applying NARMA controller for the purpose of enhancement of turbine performance.
15
SONG, YANGPO, and XIAOQI PENG. "MODELING METHOD USING COMBINED ARTIFICIAL NEURAL NETWORK." International Journal of Computational Intelligence and Applications 10, no. 02 (2011): 189–98. http://dx.doi.org/10.1142/s1469026811003057.
Full textAbstract:
To improve the modeling performance — such as accuracy and robustness — of artificial neural network (ANN), a new combined ANN and corresponding optimal modeling method are proposed in this paper. The combined ANN consists of two parallel sub-networks, and methods such as "early stopping" and "data resampling" are jointly used in training process to reduce the sensitivity of the modeling performance to its structure. To achieve better performance, the structure of combined ANN is proposed to be adjusted dynamically according to the information of expectation error and real error. Simulation experimental results verify that the optimal modeling method using combined ANN can achieve much better performance than the traditional method.
16
Rajesh, CVS, and Pardhasaradhi Nadikoppula. "Analysis of Artificial Neural Network." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 418–28. https://doi.org/10.31142/ijtsrd18482.
Full textAbstract:
An Artificial Neural Network ANN is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing. In this blog post we will try to develop an understanding of a particular type of Artificial Neural Network called the Multi Layer Perceptron. An Artificial Neural Network ANN is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements neurons working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true for ANNs as well. Rajesh CVS | Nadikoppula Pardhasaradhi "Analysis of Artificial Neural-Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18482.pdf
17
Zhao, Wei Guo, and Li Ying Wang. "Daily Discharge Prediction Using Artificial Neural Networks." Applied Mechanics and Materials 29-32 (August 2010): 2799–803. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2799.
Full textAbstract:
A daily discharge prediction system is developed by the Artificial Neural Networks (ANN) using real daily discharge data, ANN have great generalization ability and guarantee global minima for given training data. In the paper, with 8 years long-term daily information, the ANN can construct a very high precision daily discharge forecasting system. The experiment shows that the predicted curve well regresses the observed curve. It can be concluded that this technique can be seen as a very promising option to solve nonlinear regression.
18
Zou, Yizhuang, Yucun Shen, Liang Shu, et al. "Artificial Neural Network to Assist Psychiatric Diagnosis." British Journal of Psychiatry 169, no. 1 (1996): 64–67. http://dx.doi.org/10.1192/bjp.169.1.64.
Full textAbstract:
BackgroundArtificial Neural Network (ANN), as a potential powerful classifier, was explored to assist psychiatric diagnosis of the Composite International Diagnostic Interview (CIDI).MethodBoth Back-Propagation (BP) and Kohonen networks were developed to fit psychiatric diagnosis and programmed (using 60 cases) to classify neurosis, schizophrenia and normal people. The programmed networks were cross-tested using another 222 cases. All subjects were randomly selected from two mental hospitals in Beijing.ResultsCompared to ICD-10 diagnosis by psychiatrists, the overall kappa of BP network was 0.94 and that of Kohonen was 0.88 (both P < 0.01). In classifying patients who were difficult to diagnose, the kappa of BP was 0.69 (P < 0.01). ANN-assisted CIDI was compared with expert system assisted CIDI (kappa=0.72–0.76); ANN was more powerful than a traditional expert system.ConclusionANN might be used to improve psychiatric diagnosis.
19
Суханова, Наталия, and Nataliya Sukhanova. "METHOD DEVELOPMENT AND INVESTIGATION FOR CONTROL OF AUTOMATED CONTROL SYSTEM WORKING CAPACITY BASED ON ARTIFICIAL NEURAL NETWORKS." Bulletin of Bryansk state technical university 2018, no. 7 (2018): 91–98. http://dx.doi.org/10.30987/article_5ba8a190c4b385.22437052.
Full textAbstract:
The purpose of the work consists in the im-provement of known control methods of automated control system working capacity. A possibility for con-trol of automated control system working capacity based on artificial neural networks (ANN) is shown. ANN must be taught. ANN training requires a large training sample, substantial costs, computer resources, time and has high labor intensity. Methods of investigation are modeling, methods of artificial intelligence, artificial neural networks. As a result of the investigation there is devel-oped and investigated a method of automated control system (ACS) working capacity control with the use of a neural network.
20
Bondarenko, Andrey, Arkady Borisov, and Ludmila Alekseeva. "Neurons vs Weights Pruning in Artificial Neural Networks." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 22. http://dx.doi.org/10.17770/etr2015vol3.166.
Full textAbstract:
<p class="R-AbstractKeywords">Artificial neural networks (ANN) are well known for their good classification abilities. Recent advances in deep learning imposed second ANN renaissance. But neural networks possesses some problems like choosing hyper parameters such as neuron layers count and sizes which can greatly influence classification rate. Thus pruning techniques were developed that can reduce network sizes, increase its generalization abilities and overcome overfitting. Pruning approaches, in contrast to growing neural networks approach, assume that sufficiently large ANN is already trained and can be simplified with acceptable classification accuracy loss.</p><p class="R-AbstractKeywords">Current paper compares nodes vs weights pruning algorithms and gives experimental results for pruned networks accuracy rates versus their non-pruned counterparts. We conclude that nodes pruning is more preferable solution, with some sidenotes.</p>
21
Cavallaro, Lucia, Ovidiu Bagdasar, Pasquale De Meo, Giacomo Fiumara, and Antonio Liotta. "Artificial neural networks training acceleration through network science strategies." Soft Computing 24, no. 23 (2020): 17787–95. http://dx.doi.org/10.1007/s00500-020-05302-y.
Full textAbstract:
AbstractThe development of deep learning has led to a dramatic increase in the number of applications of artificial intelligence. However, the training of deeper neural networks for stable and accurate models translates into artificial neural networks (ANNs) that become unmanageable as the number of features increases. This work extends our earlier study where we explored the acceleration effects obtained by enforcing, in turn, scale freeness, small worldness, and sparsity during the ANN training process. The efficiency of that approach was confirmed by recent studies (conducted independently) where a million-node ANN was trained on non-specialized laptops. Encouraged by those results, our study is now focused on some tunable parameters, to pursue a further acceleration effect. We show that, although optimal parameter tuning is unfeasible, due to the high non-linearity of ANN problems, we can actually come up with a set of useful guidelines that lead to speed-ups in practical cases. We find that significant reductions in execution time can generally be achieved by setting the revised fraction parameter ($$\zeta $$ ζ ) to relatively low values.
22
Schuster, Alfons. "A world according to artificial neural networks." Journal of Telecommunications and Information Technology, no. 3 (September 30, 2003): 102–7. http://dx.doi.org/10.26636/jtit.2003.3.184.
Full textAbstract:
This paper presents results from a preliminary study in the field of artificial neural networks (ANN). The overall aim of our work relates to the field of cognitive science. In this wider framework we try to investigate, reason about, and model cognitive processes in order to obtain a better understanding of the major processing device involved - the human brain. In terms of content this paper presents a novel ANN learning approach. Note that throughout the paper we assume supervised learning. In contrast to the classical ANN learning approach where an ANN algorithm alters an initial random weight assignment until a reasonable solution to a problem is obtained this approach does not alter the initial random weight assignment at all, but provides a solution to the problem by transforming the actual input data. The approach is applied to perceptrons and adalines and its quality is demonstrated on simple classification problems.
23
Brasileiro, Bruno Portela, Caillet Dornelles Marinho, Paulo Mafra de Almeida Costa, Cosme Damião Cruz, Luiz Alexandre Peternelli, and Márcio Henrique Pereira Barbosa. "Selection in sugarcane families with artificial neural networks." Crop Breeding and Applied Biotechnology 15, no. 2 (2015): 72–78. http://dx.doi.org/10.1590/1984-70332015v15n2a14.
Full textAbstract:
The objective of this study was to evaluate Artificial Neural Networks (ANN) applied in an selection process within sugarcane families. The best ANN model produced no mistake, but was able to classify all genotypes correctly, i.e., the network made the same selective choice as the breeder during the simulation individual best linear unbiased predictor (BLUPIS), demonstrating the ability of the ANN to learn from the inputs and outputs provided in the training and validation phases. Since the ANN-based selection facilitates the identification of the best plants and the development of a new selection strategy in the best families, to ensure that the best genotypes of the population are evaluated in the following stages of the breeding program, we recommend to rank families by BLUP, followed by selection of the best families and finally, select the seedlings by ANN, from information at the individual level in the best families.
24
De Groff, Dolores, and Perambur Neelakanta. "Faster Convergent Artificial Neural Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 1 (2018): 7126–32. http://dx.doi.org/10.24297/ijct.v17i1.7106.
Full textAbstract:
Proposed in this paper is a novel fast-convergence algorithm applied to neural networks (ANNs) with a learning rate based on the eigenvalues of the associated Hessian matrix of the input data. That is, the learning rate applied to the backpropagation algorithm changes dynamically with the input data used for training. The best choice of learning rate to converge to an accurate value quickly is derived. This newly proposed fast-convergence algorithm is applied to a traditional multilayer ANN architecture with feed-forward and backpropagation techniques. The proposed strategy is applied to various functions learned by the ANN through training. Learning curves obtained using calculated learning rates according to the novel method proposed are compared to learning curves utilizing an arbitrary learning rate to demonstrate the usefulness of this novel technique. This study shows that convergence to accurate values can be achieved much more quickly (a reduction in iterations by a factor of hundred) using the techniques proposed here. This approach is illustrated in this research work with derivations and pertinent examples to illustrate the method and learning curves obtained.
25
Baranova, A., A. Astafiev, and E. Korchagin. "Artificial neural networks and translation." Bulletin of Science and Practice, no. 6 (June 15, 2017): 349–52. https://doi.org/10.5281/zenodo.808888.
Full textAbstract:
Relevance of the issue under study is enough high to worry and speculate about it, because technologies now are reaching the step, there it was not expected to see about 5–6 years ago. The purpose of the article is to understand, is it possible in the near future to make machine translator based Artificial Neural Network (ANN) able to remove live translators. The leading approach to the study is to compare statistical translation, translation by neural network and translation by professional live translator, to see how high quality of translation by machine interpreter. The article considers the basic concepts of ANN, as well as modern artificial translators. The results showed that Artificial Intelligence developed on a very high level, but we are still at a great distance from technologies of that level and near future, it will eliminate professional translators, though, artificial translators influence on increasing level of language knowledge. The article may be useful for people interested in mechanisms of translation and for translators of all levels, as well as for programmers who are interested in Artificial Intelligence (AI) and all kind of translators, who want to know the future of their profession.
26
Zlotin, Boris L., Vladimir N. Proseanic, Boris S. Farber, et al. "A new approach to building artificial neural networks and medicine." Annals of Mechnikov Institute, no. 4 (December 8, 2021): 101–7. https://doi.org/10.5281/zenodo.5767079.
Full textAbstract:
Numerous attempts to use neural networks in medicine remain unsuccessful to this day because of an old mistake in the development of neural networks. In 1954, Frank Rosenblatt, created the first artificial neural network - the perceptron based on an understanding of the operation of brain neurons. This was a brilliant achievement; however, lack of knowledge at the time on how biological neurons worked led to systematic errors in the perceptron design and methods of training. These errors have been repeatedly propagated in most artificial neural networks (ANN). The article describes the conceptual design of a brand-new type of perceptron named PANN (Progressive Artificial Neural Network), free from systematic errors of classical ANN and therefore with various unique properties. The article also provides data of the PANN network testing and a link that allows direct testing of the proposed neural network.
27
M., Ankith, Surya Teja S. P., and Damodharan N. "ARTIFICIAL NEURAL NETWORKS: FUNCTIONINGANDAPPLICATIONS IN PHARMACEUTICAL INDUSTRY." International Journal of Applied Pharmaceutics 10, no. 5 (2018): 28. http://dx.doi.org/10.22159/ijap.2018v10i5.28300.
Full textAbstract:
Artificial Neural Network (ANN) technology is a group of computer designed algorithms for simulating neurological processing to process information and produce outcomes like the thinking process of humans in learning, decision making and solving problems. The uniqueness of ANN is its ability to deliver desirable results even with the help of incomplete or historical data results without a need for structured experimental design by modeling and pattern recognition. It imbibes data through repetition with suitable learning models, similarly to humans, without actual programming. It leverages its ability by processing elements connected with the user given inputs which transfers as a function and provides as output. Moreover, the present output by ANN is a combinational effect of data collected from previous inputs and the current responsiveness of the system. Technically, ANN is associated with highly monitored network along with a back propagation learning standard. Due to its exceptional predictability, the current uses of ANN can be applied to many more disciplines in the area of science which requires multivariate data analysis. In the pharmaceutical process, this flexible tool is used to simulate various non-linear relationships. It also finds its application in the enhancement of pre-formulation parameters for predicting physicochemical properties of drug substances. It also finds its applications in pharmaceutical research, medicinal chemistry, QSAR study, pharmaceutical instrumental engineering. Its multi-objective concurrent optimization is adopted in the drug discovery process, protein structure, rational data analysis also.
28
Rajesh, CVS. "Basics and Features of Artificial Neural Networks." International Journal of Trend in Scientific Research and Development 2, no. 2 (2018): 1065–69. https://doi.org/10.31142/ijtsrd9578.
Full textAbstract:
The models of the computing for the perform the pattern recognition methods by the performance and the structure of the biological neural network. A network consists of computing units which can display the features of the biological network. In this paper, the features of the neural network that motivate the study of the neural computing are discussed and the differences in processing by the brain and a computer presented, historical development of neural network principle, artificial neural network ANN terminology, neuron models and topology are discussed. Rajesh CVS | M. Padmanabham "Basics and Features of Artificial Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: https://www.ijtsrd.com/papers/ijtsrd9578.pdf
29
ZHU, LIANG, PAUL SCHONFELD, YEON MYUNG KIM, IAN FLOOD, and CHING-JUNG TING. "Queuing network analysis for waterways with artificial neural networks." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, no. 5 (1999): 365–75. http://dx.doi.org/10.1017/s0890060499135017.
Full textAbstract:
An Artificial Neural Network (ANN) model has been developed for analyzing traffic in an inland waterway network. The main purpose of this paper is to determine how well such a relatively fast model for analyzing a queuing network could substitute for far more expensive simulation. Its substitutability for simulation is judged by relative discrepancies in predicting tow delays between the ANN and simulation models. This model is developed by integrating five distinct ANN submodels that predict tow headway variances at (1) merge points, (2) branching (i.e., diverging) points, (3) lock exits, and (4) link outflow points (e.g., at ports, junctions, or lock entrances), plus (5) tow queuing delays at locks. Preliminary results are shown for those five submodels and for the integrated network analysis model. Eventually, such a network analyzer should be useful for designing, selecting, sequencing, and scheduling lock improvement projects, for controlling lock operations, for system maintenance planning, and for other applications where many combinations of network characteristics must be evaluated. More generally, this method of decomposing complex queuing networks into elements that can be analyzed with ANNs and then recombined provides a promising approach for analyzing other queuing networks (e.g., in transportation, communication, computing, and production systems).
30
Schulze, F. H., H. Wolf, H. W. Jansen, and P. van der Veer. "Applications of artificial neural networks in integrated water management: fiction or future?" Water Science and Technology 52, no. 9 (2005): 21–31. http://dx.doi.org/10.2166/wst.2005.0279.
Full textAbstract:
An Artificial Neural Network (ANN) is nowadays recognized as a very promising tool for relating input data to output data. It is said that the possibilities of artificial neural networks are unlimited. Here we focus on the potential role of neural networks in integrated water management. An Artificial Neural Network (ANN) is a mathematical methodology which describes relations between cause (input data) and effects (output data) irrespective of the process laying behind and without the need for making assumptions considering the nature of the relations. The applications are widespread and vary from optimization of measuring networks, operational water management, prediction of drinking water consumption, on-line steering of wastewater treatment plants and sewage systems, up to more specific applications such as establishing a relationship between the observed erosion of groyne field sediments and the characteristics of passing vessels on the river Rhine. Especially where processes are complex, neural networks can open new possibilities for understanding and modelling these kinds of complex processes. Besides explaining the method of ANN this paper shows different applications. Three examples have been worked out in more detail. An intelligent monitoring system is shown for the on-line prediction of water consumption, ANN are successfully used for sludge cost monitoring and optimizing wastewater treatment and the usage of ANN is shown in optimizing and monitoring water quality measuring networks. An ANN appears to be a multiuse and powerful tool for modelling complex processes.
31
Begum, Afsana, Md Masiur Rahman, and Sohana Jahan. "Medical diagnosis using artificial neural networks." Mathematics in Applied Sciences and Engineering 5, no. 2 (2024): 149–64. http://dx.doi.org/10.5206/mase/17138.
Full textAbstract:
Medical diagnosis using Artificial Neural Networks (ANN) and computer-aided diagnosis with deep learning is currently a very active research area in medical science. In recent years, for medical diagnosis, neural network models are broadly considered since they are ideal for recognizing different kinds of diseases including autism, cancer, tumor lung infection, etc. It is evident that early diagnosis of any disease is vital for successful treatment and improved survival rates. In this research, five neural networks, Multilayer neural network (MLNN), Probabilistic neural network (PNN), Learning vector quantization neural network (LVQNN), Generalized regression neural network (GRNN), and Radial basis function neural network (RBFNN) have been explored. These networks are applied to several benchmarking data collected from the University of California Irvine (UCI) Machine Learning Repository. Results from numerical experiments indicate that each network excels at recognizing specific physical issues. In the majority of cases, both the Learning Vector Quantization Neural Network and the Probabilistic Neural Network demonstrate superior performance compared to the other networks.
32
Lee, Yongjei, Sungchil Lee, and Hun-Kyun Bae. "Design of Jetty Piles Using Artificial Neural Networks." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/405401.
Full textAbstract:
To overcome the complication of jetty pile design process, artificial neural networks (ANN) are adopted. To generate the training samples for training ANN, finite element (FE) analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN) with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost.
33
Konakoglu, B., L. Cakır, and E. Gökalp. "2D COORDINATE TRANSFORMATION USING ARTIFICIAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W1 (October 26, 2016): 183–86. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w1-183-2016.
Full textAbstract:
Two coordinate systems used in Turkey, namely the ED50 (European Datum 1950) and ITRF96 (International Terrestrial Reference Frame 1996) coordinate systems. In most cases, it is necessary to conduct transformation from one coordinate system to another. The artificial neural network (ANN) is a new method for coordinate transformation. One of the biggest advantages of the ANN is that it can determine the relationship between two coordinate systems without a mathematical model. The aim of this study was to investigate the performances of three different ANN models (Feed Forward Back Propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Radial Basis Function Neural Network (RBFNN)) with regard to 2D coordinate transformation. To do this, three data sets were used for the same study area, the city of Trabzon. The coordinates of data sets were measured in the ED50 and ITRF96 coordinate systems by using RTK-GPS technique. Performance of each transformation method was investigated by using the coordinate differences between the known and estimated coordinates. The results showed that the ANN algorithms can be used for 2D coordinate transformation in cases where optimum model parameters are selected.
34
Smith, Joe. "Astrometric Binary Classification via Artificial Neural Networks." Astrophysical Journal 974, no. 1 (2024): 96. http://dx.doi.org/10.3847/1538-4357/ad7731.
Full textAbstract:
Abstract With nearly two billion stars observed and their corresponding astrometric parameters evaluated in the recent Gaia mission, the number of astrometric binary candidates has risen significantly. Due to the surplus of astrometric data, the current computational methods employed to inspect these astrometric binary candidates are both computationally expensive and cannot be executed in a reasonable time frame. In light of this, a machine learning (ML) technique to automatically classify whether a set of stars belongs to an astrometric binary pair via an artificial neural network (ANN) is proposed. Using data from Gaia Data Release 3, the ANN was trained and tested on 1.5 million highly probable true and visual binaries, considering the proper motions, parallaxes, and angular and physical separations as features. The ANN achieves high classification scores, with an accuracy of 99.3%, a precision rate of 0.988, a recall rate of 0.991, and an area under the curve of 0.999, indicating that the utilized ML technique is a highly effective method for classifying astrometric binaries. Thus, the proposed ANN is a promising alternative to the existing methods for the classification of astrometric binaries.
35
Alam, M. N., Amin Sallem, Pedro Pereira, Benhala Bachir, and Nouri Masmoudi. "Optimal Artificial Neural Network using Particle Swarm Optimization." E3S Web of Conferences 469 (2023): 00019. http://dx.doi.org/10.1051/e3sconf/202346900019.
Full textAbstract:
Artificial neuron networks (ANNs) are widely used for data analyticS in broad areas of engineering applications and commercial services. The ANN has one to two hidden layers. In advanced ANN, multiple-layer ANN is used where the network extracts different features until it can recognize what it is looking for through deep learning approaches. Usually, a backpropagation algorithm is used to train the network and fix weights and biases associated with each network neuron. This paper proposes a particle swarm optimization (PSO) based algorithm for training ANN for better performance and accuracy. Two types of ANN models and their training using PSO have been developed. The performance of the developed models has been analyzed on a standard dataset. Also, the effectiveness and suitability of the developed approach have been demonstrated through statistics of the obtained results.
36
Fedor, P., I. Malenovský, J. Vaňhara, W. Sierka, and J. Havel. "Thrips (Thysanoptera) identification using artificial neural networks." Bulletin of Entomological Research 98, no. 5 (2008): 437–47. http://dx.doi.org/10.1017/s0007485308005750.
Full textAbstract:
AbstractWe studied the use of a supervised artificial neural network (ANN) model for semi-automated identification of 18 common European species of Thysanoptera from four genera: Aeolothrips Haliday (Aeolothripidae), Chirothrips Haliday, Dendrothrips Uzel, and Limothrips Haliday (all Thripidae). As input data, we entered 17 continuous morphometric and two qualitative two-state characters measured or determined on different parts of the thrips body (head, pronotum, forewing and ovipositor) and the sex. Our experimental data set included 498 thrips specimens. A relatively simple ANN architecture (multilayer perceptrons with a single hidden layer) enabled a 97% correct simultaneous identification of both males and females of all the 18 species in an independent test. This high reliability of classification is promising for a wider application of ANN in the practice of Thysanoptera identification.
37
Jiménez, Rafael, Elena Gervilla, Albert Sesé, Juan José Montaño, Berta Cajal, and Alfonso Palmer. "Dimensionality Reduction in Data Mining Using Artificial Neural Networks." Methodology 5, no. 1 (2009): 26–34. http://dx.doi.org/10.1027/1614-2241.5.1.26.
Full textAbstract:
The use of classic dimension reduction techniques can be considered customary practice within the context of data mining (DM). Nevertheless, although artificial neural networks (ANNs) are one of the most important DM techniques, specific ANN architectures for dimensionality reduction, such as the principal components analysis ANN (PCA-ANN) and the linear auto-associative ANN (LA-ANN), are used on far fewer occasions. In this study, categorical principal component analysis (CATPCA) and the two ANN procedures are studied and compared searching for uniqueness in an applied context relative to personality variables and drug consumption. A sample of 7,030 adolescents completed a personality test made up of 20 dichotomous items with a hypothesized four-factor latent model. Results point out that both ANN factor solutions converge to those obtained using CATPCA. Nevertheless, possible drawbacks of the ANN techniques lie in their relatively complex application, as well as in the need to use visual graphic analysis as a support for interpreting the factorized solutions.
38
A, Mohamed Sikkander, R.RamaNachiar, and Yasmeen Khadeeja. "Artificial Neural Networks (ANNs) in Lungs Cancer Detection." International Journal of Scientific Research and Innovative Studies 1, no. 1 (2022): 155–58. https://doi.org/10.5281/zenodo.6878941.
Full textAbstract:
The research looks into the use of an Artificial Neural Network model to detect the presence of lung cancer in someone's body. We developed an Artificial Neural Network (ANN) in this paper to detect the presence or absence of lung cancer in the human body. Symptoms such as yellow fingers, anxiety, chronic disease, fatigue, allergy, and wheezing, coughing, shortness of breath, swallowing difficulty, and chest pain were used to diagnose lung cancer. They were used as input variables for our ANN, along with other information about the person. Our ANN was created, trained, and validated using the data set "survey lung cancer." The ANN model was found to be 96.67 percent accurate in detecting the absence or presence of lung cancer.
39
Chiru-Danzer, M., C. H. Juang, R. A. Christopher, and J. Suber. "Estimation of liquefaction-induced horizontal displacements using artificial neural networks." Canadian Geotechnical Journal 38, no. 1 (2001): 200–207. http://dx.doi.org/10.1139/t00-087.
Full textAbstract:
In the present study, artificial neural network (ANN) models based on field performance data are developed for predicting liquefaction-induced horizontal displacements. A database consisting of 443 measurements of horizontal displacements forms the basis for ANN modeling and analysis. The ANN model resulted in predictive capabilities that surpass those of published methods. A sensitivity analysis of the ANN model is conducted to evaluate the effect of each individual input variable on the calculated horizontal displacement. The newly developed ANN model is compared with and shown to be more accurate than other existing methods in predicting liquefaction-induced horizontal displacements.Key words: liquefaction, artificial neural networks, lateral spreading.
40
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.
Full textAbstract:
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.
41
Shank, D. B., G. Hoogenboom, and R. W. McClendon. "Dewpoint Temperature Prediction Using Artificial Neural Networks." Journal of Applied Meteorology and Climatology 47, no. 6 (2008): 1757–69. http://dx.doi.org/10.1175/2007jamc1693.1.
Full textAbstract:
Abstract Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans.
42
Vivek, C., M. Indu, and N. Nandhini. "Speech Recognition Using Artificial Neural Network." Journal of Cognitive Human-Computer Interaction 5, no. 2 (2023): 08–14. http://dx.doi.org/10.54216/jchci.050201.
Full textAbstract:
Speech is a verbal communication used by humans through language. Likewise speech recognition is a process of converting speech to text. This paper provides a study of use of artificial neural networks(ANN) in speech recognition. Hidden Markov models (HMM) is a traditional statistical techniques for performing speech recognition. In speech detection software, Mel frequency cepstral coefficients (MFCCs) are frequently used. With different approaches evolving, we deal with the features used to recognize the speech pattern and implementation of speech recognition in the efficient types of artificial neural network (ANN).
43
Pavić, Ivica, Frano Tomašević, and Ivana Damjanović. "Application of artificial neural networks for external network equivalent modeling." Journal of Energy - Energija 64, no. 1-4 (2022): 275–84. http://dx.doi.org/10.37798/2015641-4156.
Full textAbstract:
In this paper an artificial neural network (ANN) based methodology is proposed for determining an external network equivalent. The modified Newton-Raphson method with constant interchange of total active power between internal and external system is used for solving the load flow problem. A multilayer perceptron (MLP) with backpropagation training algorithm is applied for external network determination. The proposed methodology was tested with the IEEE 24-bus test network and simulation results show a very good performance of the ANN for external network modeling.
44
Krasniuk, S. O. "ARTIFICIAL NEURAL NETWORKS IN MACHINE LINGUISTICS." SWorld-Ger Conference proceedings, gec37-00 (February 28, 2022): 107–12. https://doi.org/10.30890/2709-1783.2025-37-00-016.
Full textAbstract:
Artificial neural networks (ANN) have revolutionized natural language processing (NLP) and have fundamentally changed the approach to solving linguistic problems. Due to their ability to learn from large amounts of data, ANNs demonstrate high performance
45
Niu, Jiale, and Yingying Zhang. "Application of Artificial Neural Networks in Polymer Composites: A Review." Asian Journal of Research in Computer Science 16, no. 4 (2023): 67–79. http://dx.doi.org/10.9734/ajrcos/2023/v16i4371.
Full textAbstract:
Artificial neural networks (ANN), which have been a hot topic in the field of artificial intelligence (AI) since the 1980s, are widely applied these years for their strong ability in the field of nonlinear mapping, pattern recognition, robots, automatic control, biology, economy and so on. This review presents and summarizes the history of artificial neural networks, briefly introducing the application of artificial neural networks. After that, the paper focuses on an overview of research advances in neural networks for polymer composites and introduces several classical categories of applications. Finally, we look ahead to the development of neural network applications in polymer composites and provide a future outlook for the application of artificial neural network in polymer composites.
46
Ibragimov, Sanjarbek, and Asror Boytemirov. "PREDICTION OF PERMEABILITY OF OIL AND GAS LAYERS USING ARTIFICIAL NEURAL NETWORKS." International Journal of Advance Scientific Research 05, no. 12 (2024): 290–95. https://doi.org/10.37547/ijasr-04-12-45.
Full textAbstract:
This article focuses on predicting the permeability of oil and gas reservoirs using artificial neural networks (ANN). By utilizing data sets from oil and gas wells, comprehensive preprocessing was conducted, including feature selection, scaling, and normalization to ensure the robustness of the models. The effectiveness of ANN in predicting the permeability of underground formations was evaluated using petrophysical data from wells in the Bukhara-Khiva oil and gas region. A precise permeability prediction model was created using key petrophysical parameters such as gamma rays (GR), resistivity (RT), sonic (DT), density (RHOB), and neutron porosity (NPHI). To enhance model performance, the dataset underwent complete preprocessing, including normalization and feature selection. The model's performance was assessed through MSE, R², and MAE metrics, demonstrating higher accuracy compared to traditional linear regression models. The results indicate that the ANN model provides highly accurate permeability predictions. The findings offer valuable insights for optimizing exploration and production strategies in the oil and gas industry, highlighting the superiority of machine learning and neural network models over traditional methods in subsurface resource evaluation.
47
Liu, Fangxin, Wenbo Zhao, Yongbiao Chen, Zongwu Wang, and Li Jiang. "SpikeConverter: An Efficient Conversion Framework Zipping the Gap between Artificial Neural Networks and Spiking Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1692–701. http://dx.doi.org/10.1609/aaai.v36i2.20061.
Full textAbstract:
Spiking Neural Networks (SNNs) have recently attracted enormous research interest since their event-driven and brain-inspired structure enables low-power computation. In image recognition tasks, the best results are achieved by SNN so far utilizing ANN-SNN conversion methods that replace activation functions in artificial neural networks~(ANNs) with integrate-and-fire neurons. Compared to source ANNs, converted SNNs usually suffer from accuracy loss and require a considerable number of time steps to achieve competitive accuracy. We find that the performance degradation of converted SNN stems from the fact that the information capacity of spike trains in transferred networks is smaller than that of activation values in source ANN, resulting in less information being passed during SNN inference. To better correlate ANN and SNN for better performance, we propose a conversion framework to mitigate the gap between the activation value of source ANN and the generated spike train of target SNN. The conversion framework originates from exploring an identical relation in the conversion and exploits temporal separation scheme and novel neuron model for the relation to hold. We demonstrate almost lossless ANN-SNN conversion using SpikeConverter for VGG-16, ResNet-20/34, and MobileNet-v2 SNNs on challenging datasets including CIFAR-10, CIFAR-100, and ImageNet. Our results also show that SpikeConverter achieves the abovementioned accuracy across different network architectures and datasets using 32X - 512X fewer inference time-steps than state-of-the-art ANN-SNN conversion methods.
48
Wang, Yang. "Harnessing Advanced Neural Architectures: A Comprehensive Approach to Stock Market Prediction Using ANN, BPNN, and GAN." SHS Web of Conferences 196 (2024): 02002. http://dx.doi.org/10.1051/shsconf/202419602002.
Full textAbstract:
The advent of advanced neural network models has revolutionized the field of machine learning, enabling breakthroughs in various domains such as computer vision, natural language processing, and predictive analytics. This paper introduces three pivotal neural network architectures: Artificial Neural Networks (ANN), Back-Propagation Neural Networks (BPNN), and Generative Adversarial Networks (GAN). We explore the theoretical underpinnings, practical applications, and the significance of these models in the broader context of artificial intelligence research and industry.
49
Chauhan, Seema, and R. K. Shrivastava. "Reference evapotranspiration forecasting using different artificial neural networks algorithms." Canadian Journal of Civil Engineering 36, no. 9 (2009): 1491–505. http://dx.doi.org/10.1139/l09-074.
Full textAbstract:
The present study aims to apply artificial neural networks (ANNs) for reference evapotranspiration (ETo) prediction. Three different feed-forward artifical neural network (ANN) models, each using varied input combinations of previous months ETo, have been trained and tested. The output of the network was the one-month-ahead ETo. The networks learned to forecast one-month-ahead ETo for Mahanadi reservoir project area using the three learning methods namely quasi-Newton algorithm, Levenberg–Marquardt algorithm and backpropagation with adaptive learning rate algorithm. The training results were compared with each other, and performance evaluations were done for untrained data. The performance evaluations measured were standard error of estimates (SEE), raw standard error of estimates (RSEE), and model efficiency. The best ANN architecture for prediction of ETo was obtained for Mahanadi reservoir project area. The monthly reference evapotranspiration data were estimated by the Penman–Monteith method and used for training and testing of the ANN models. Further ANNs predicted results were compared with those obtained using the statistical multiple regression technique. Based on results obtained, the ANN model with architecture of 3–9-1 (three, nine, and one neuron(s) in the input, hidden, and output layers, respectively) trained using quasi-Newton algorithm was found to be the best amongst all the models with minimum SEE and RSEE of 0.45 and 0.45 mm/d respectively and maximum model efficiency of 93%. It is concluded that ANN can be used to predict ETo.
50
Singh, A. K., M. C. Deo, and V. S. Kumar. "Prediction of littoral drift with artificial neural networks." Hydrology and Earth System Sciences Discussions 4, no. 4 (2007): 2497–519. http://dx.doi.org/10.5194/hessd-4-2497-2007.
Full textAbstract:
Abstract. The amount of sand moving parallel to a coastline forms a prerequisite for many harbour design projects. Such information is currently obtained through various empirical formulae. Despite much research in the past an accurate and reliable estimation of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN). Feed forward networks were developed to predict the sand drift from a variety of causative variables. The best network was selected after trying out many alternatives. In order to improve the accuracy further its outcome was used to develop another network. Such simple two-stage training yielded most satisfactory results. An equation combining the network and a non-linear regression is presented for quick field usage. An attempt was made to see how both ANN and statistical regression differ in processing the input information. The network was validated by confirming its consistency with the underlying physical process.