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Relevant bibliographies by topics / ANN (Artificial Neural Network)

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Academic literature on the topic 'ANN (Artificial Neural Network)'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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Journal articles on the topic "ANN (Artificial Neural Network)"

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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.

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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.

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Yang, Judy X., Lily D. Li, and Mohammad G. Rasul. "A Conceptual Artificial Neural Network Model in Warehouse Receiving Management." International Journal of Machine Learning and Computing 11, no. 2 (2021): 130–36. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1025.

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The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

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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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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.

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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.

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Havryliuk, Volodymyr. "Artificial neural network based detection of neutral relay defects." MATEC Web of Conferences 294 (2019): 03001. http://dx.doi.org/10.1051/matecconf/201929403001.

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The problem considered in the work is concerned to the automatic detecting and identifying defects in a neutral relay. The special design of electromechanical neutral relays is responsible for the strong asymmetry of its output signal for all possible safety-critical influences, and therefore neutral relays have negligible values of dangerous failures rate. To ensure the safe operation of relay-based train control systems, electromechanical relays should be periodically subjected to routine maintenance, during which their main operating parameters are measured, and the relays are set up in accordance with technical regulations. These measurements are mainly done manually, so they take a lot of time (up to four hours per relay), are expensive, and the results are subjective. In recent years, fault diagnosis methods based on artificial neural networks (ANN) have received considerable attention. The ANN-based classification of relay defects using the time dependence of the transient current in the relay coil during its switching is very promising for practical utilization, but for efficient use of ANN a lot of data is required to train the artificial neural network. To reduce the ANN training time, a pre-processing of the time dependence of relay transient current was proposed using wavelet transform and wavelet energy entropy, which makes it possible to reveal the features of the main defects of the relay armature, contact springs, and magnetic system. The effectiveness of the proposed approach for automatic detecting and identifying of the neutral relays defects was confirmed during testing of the relays with various artificially created defects.

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Aziz, Mustafa Nizamul. "A Review on Artificial Neural Networks and its’ Applicability." Bangladesh Journal of Multidisciplinary Scientific Research 2, no. 1 (2020): 48–51. http://dx.doi.org/10.46281/bjmsr.v2i1.609.

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The field of artificial neural networks (ANN) started from humble beginnings in the 1950s but got attention in the 1980s. ANN tries to emulate the neural structure of the brain, which consists of several thousand cells, neuron, which is interconnected in a large network. This is done through artificial neurons, handling the input and output, and connecting to other neurons, creating a large network. The potential for artificial neural networks is considered to be huge, today there are several different uses for ANN, ranging from academic research in such fields as mathematics and medicine to business-based purposes and sports prediction. The purpose of this paper is to give words to artificial neural networks and to show its applicability. Documents analysis was used here as the data collection method. The paper figured out network structures, steps for constructing an ANN, architectures, and learning algorithms.

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Jayadianti, Herlina, Tedy Agung Cahyadi, Nur Ali Amri, and Muhammad Fathurrahman Pitayandanu. "METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW." Jurnal Tekno Insentif 14, no. 2 (2020): 48–53. http://dx.doi.org/10.36787/jti.v14i2.150.

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Abstrak - Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang. Review dilakukan dengan membandingkan hasil dari setiap model yang telah dilakukan pada beberapa penelitian sebelumnya. Penelitian ini menggunakan metode kuantitatif. Model yang dibandingkan pada penelitian di antaranya yaitu model Fuzzy, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR-Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), dan Artificial Neural Network-Fuzzy (ANN-Fuzzy). Hasil dari review menyimpulkan bahwa model Artificial Neural Network memiliki beberapa kelebihan dibandingkan dengan metode yang lain, yakni ANN mampu memberikan hasil yang dapat mengenali pola-pola dengan baik dan mudah dikembangkan menjadi bermacam-macam variasi sesuai dengan permasalahan maupun parameter yang ada, sehingga ANN direkomendasikan untuk perhitungan prediksi hujan. Abstract - Various kinds of research have been carried out to find accurate models to predict rainfall in various fields, so the research that has been done previously was reviewed again to help the drainage process in mining companies. The review is done by comparing the results of each model that has been conducted in several previous studies. This research used quantitative methods. Models compared in this study include the Fuzzy model, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR -Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network-Fuzzy (ANN-Fuzzy). The results of the review concluded that the Artificial Neural Network model has several advantages compared to other methods, namely ANN is able to provide results that can recognize patterns well and easily developed into a variety of variations in accordance with existing problems and parameters, so ANN is recommended for rain prediction calculation.

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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.

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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.

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Hussein, Maryam Mahmood, Ammar Hussein Mutlag, and Hussain Shareef. "Developed artificial neural network based human face recognition." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (2019): 1279. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1279-1285.

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<p>Face recognition has become one of the most important challenging problems in personal computer-human interaction, video observation, and biometric. Many algorithms have been developed in the recent years. Theses algorithms are not sufficiently robust to address the complex images. Therefore, this paper proposes soft computing algorithm based face recognition. One of the most promising soft computing algorithms which is back-propagation artificial neural network (BP-ANN) has been proposed. The proposed BP-ANN has been developed to improve the performance of the face recognition. The implementation of the developed BP-ANN has been achieved using MATLAB environment. The developed BP-ANN requires supervised training to learn how to anticipate results from the desired data. The BP-ANN has been developed to recognition 10 persons. Ten images have been used for each person. Therefore, 100 images have been utilized to train the developed BP-ANN. In this research 50 images have been used for testing purpose. The results show that the developed BP-ANN has produced a success ratio of 82%.</p>

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Mahmood, Muhammad Arif, Anita Ioana Visan, Carmen Ristoscu, and Ion N. Mihailescu. "Artificial Neural Network Algorithms for 3D Printing." Materials 14, no. 1 (2020): 163. http://dx.doi.org/10.3390/ma14010163.

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Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

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Dissertations / Theses on the topic "ANN (Artificial Neural Network)"

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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.<br>PHD

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Sun, Chang. "Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85020.

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Artificial Neural Networks (ANNs) have been established as one of the most important algorithmic tools in the Machine Learning (ML) toolbox over the past few decades. ANNs' recent rise to widespread acceptance can be attributed to two developments: (1) the availability of large-scale training and testing datasets; and (2) the availability of new computer architectures for which ANN implementations are orders of magnitude more efficient. In this thesis, I present research on two aspects of the second development. First, I present a portable, open source implementation of ANNs in OpenCL and MPI. Second, I present performance and scaling models for ANN algorithms on state-of-the-art Graphics Processing Unit (GPU) based parallel compute clusters.<br>Master of Science

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Zhao, Lichen. "Random pulse artificial neural network architecture." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0006/MQ36758.pdf.

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Ng, Justin. "Artificial Neural Network-Based Robotic Control." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1846.

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Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system.

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Joy, Karen. "Evaluating Input Variable Effects of an Artificial Neural Network Modeling Facial Attractiveness." VCU Scholars Compass, 2005. http://scholarscompass.vcu.edu/etd_retro/128.

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Artificial Neural Networks (ANNs) are powerful predictors, however, they essentially function like 'black boxes' because they lack explanatory power. Various algorithms have been developed to examine input influences and interactions thus enhancing understanding of the function being modeled. The study of facial attractiveness is one domain that could potentially benefit from ANN models. The literature shows that the relationship between attractiveness and facial attributes is complex and not yet fully understood. In this project, a feed-forward ANN was trained with backpropagation to 0.86 classification using 8-fold cross validation. The dataset consisted of 88 female facial images, each containing 17 geofacial measurements, a random noise variable, and a rating. Input 'clamping' and the Connection Weight Approach (Olden & Jackson, 2002), were implemented and the results were examined in terms of the facial attractiveness domain. In general, the results suggest that more feminized and asymmetrical features enhance facial attractiveness.

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Bonagura, Mario <1982&gt. "Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amsdottorato.unibo.it/4880/.

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The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT. The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations. Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.

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Lundin, Johan. "Prediction of Protein Mutations Using Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-400.

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

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

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Colorectal cancer is the second cause of cancer death in the world. Aiming to early detect and prevent this type of cancer, clinicians perform screenings through the colon searching for polyps (colorectal cancer precursor lesions).If found, these lesions are susceptible of being removed in order to further ana-lyze their malignancy degree. Automatic polyp segmentation is of primary impor-tance when it comes to computer-aided medical diagnosis using images obtained in colonoscopy screenings. These results allow for more precise medical diagnosis which can lead to earlier detection.This project proposed a neural network based solution for semantic segmenta-tion, using the U-net architecture.Combining different data augmentation techniques to alleviate the problem of data scarcity and conducting experiments on the different hyperparameters of the network, the U-net scored a mean Intersection over Union (IoU) of 0,6814. A final approach that combines prediction maps of different models scored a mean IoU of 0,7236.

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Bataineh, Mohammad Hindi. "Artificial neural network for studying human performance." Thesis, University of Iowa, 2012. https://ir.uiowa.edu/etd/3259.

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The vast majority of products and processes in industry and academia require human interaction. Thus, digital human models (DHMs) are becoming critical for improved designs, injury prevention, and a better understanding of human behavior. Although many capabilities in the DHM field continue to mature, there are still many opportunities for improvement, especially with respect to posture- and motion-prediction. Thus, this thesis investigates the use of artificial neural network (ANN) for improving predictive capabilities and for better understanding how and why human behave the way they do. With respect to motion prediction, one of the most challenging opportunities for improvement concerns computation speed. Especially, when considering dynamic motion prediction, the underlying optimization problems can be large and computationally complex. Even though the current optimization-based tools for predicting human posture are relatively fast and accurate and thus do not require as much improvement, posture prediction in general is a more tractable problem than motion prediction and can provide a test bead that can shed light on potential issues with motion prediction. Thus, we investigate the use of ANN with posture prediction in order to discover potential issues. In addition, directly using ANN with posture prediction provides a preliminary step towards using ANN to predict the most appropriate combination of performance measures (PMs) - what drives human behavior. The PMs, which are the cost functions that are minimized in the posture prediction problem, are typically selected manually depending on the task. This is perhaps the most significant impediment when using posture prediction. How does the user know which PMs should be used? Neural networks provide tools for solving this problem. This thesis hypothesizes that the ANN can be trained to predict human motion quickly and accurately, to predict human posture (while considering external forces), and to determine the most appropriate combination of PM(s) for posture prediction. Such capabilities will in turn provide a new tool for studying human behavior. Based on initial experimentation, the general regression neural network (GRNN) was found to be the most effective type of ANN for DHM applications. A semi-automated methodology was developed to ease network construction, training and testing processes, and network parameters. This in turn facilitates use with DHM applications. With regards to motion prediction, use of ANN was successful. The results showed that the calculation time was reduced from 1 to 40 minutes, to a fraction of a second without reducing accuracy. With regards to posture prediction, ANN was again found to be effective. However, potential issues with certain motion-prediction tasks were discovered and shed light on necessary future development with ANNs. Finally, a decision engine was developed using GRNN for automatically selecting four human PMs, and was shown to be very effective. In order to train this new approach, a novel optimization formulation was used to extract PM weights from pre-existing motion-capture data. Eventually, this work will lead to automatically and realistically driving predictive DHMs in a general virtual environment.

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

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

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Books on the topic "ANN (Artificial Neural Network)"

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Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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North Atlantic Treaty Organization. Advisory Group for Aerospace Research and Development. Artificial neural network approaches in guidance and control. AGARD, 1991.

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Zeidenberg, Matthew. Neural network models in artificial intelligence and cognition. Ellis Horwood, 1989.

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Kattan, Ali. Artificial neural network training and software implementation techniques. Nova Science Publishers, 2011.

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Delgado-Frias, José G. VLSI for Artificial Intelligence and Neural Networks. Springer US, 1991.

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

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

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Neural network design and the complexity of learning. MIT Press, 1990.

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1931-, Taylor John Gerald, and Mannion C. L. T, eds. Theory and applications of neural networks: Proceedings of the First British Neural Network Society Meeting, London. Springer-Verlag, 1992.

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Book chapters on the topic "ANN (Artificial Neural Network)"

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Majumder, Mrinmoy, and Apu K. Saha. "Artificial Neural Network." In Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-308-8_4.

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

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Medagoda, Nishantha. "Sentiment Analysis on Morphologically Rich Languages: An Artificial Neural Network (ANN) Approach." In Artificial Neural Network Modelling. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_17.

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Daniel, Gómez González. "Artificial Neural Network." In Encyclopedia of Sciences and Religions. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_200980.

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Weik, Martin H. "artificial neural network." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_860.

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Okwu, Modestus O., and Lagouge K. Tartibu. "Artificial Neural Network." In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61111-8_14.

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Asadollahfardi, Gholamreza. "Artificial Neural Network." In SpringerBriefs in Water Science and Technology. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44725-3_5.

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Zhang, Zhihua. "Artificial Neural Network." In Multivariate Time Series Analysis in Climate and Environmental Research. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67340-0_1.

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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. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_20.

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Shanmuganathan, Subana. "A Hybrid Artificial Neural Network (ANN) Approach to Spatial and Non-spatial Attribute Data Mining: A Case Study Experience." In Artificial Neural Network Modelling. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_21.

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Conference papers on the topic "ANN (Artificial Neural Network)"

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Kanwar, Nidhi, Anil Kumar Goswami, and S. P. Mishra. "Design Issues in Artificial Neural Network (ANN)." In 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). IEEE, 2019. http://dx.doi.org/10.1109/iot-siu.2019.8777337.

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Ahmed, Shohel Ali, Snigdha Dey, and Kandarpa Kumar Sarma. "Image texture classification using Artificial Neural Network (ANN)." In 2011 2nd National Conference on Emerging Trends and Applications in Computer Science (NCETACS). IEEE, 2011. http://dx.doi.org/10.1109/ncetacs.2011.5751383.

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Naim, Nani Fadzlina, Ahmad Ihsan Mohd Yassin, Nurafizah Binti Zakaria, and Norfishah Ab. Wahab. "Classification of thumbprint using Artificial Neural Network (ANN)." In 2011 IEEE International Conference on System Engineering and Technology (ICSET). IEEE, 2011. http://dx.doi.org/10.1109/icsengt.2011.5993456.

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Chowdhury, S. "Application of artificial neural network (ANN) in SF." In 11th International Symposium on High-Voltage Engineering (ISH 99). IEE, 1999. http://dx.doi.org/10.1049/cp:19990921.

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Yang, Zhun, Adam Ishay, and Joohyung Lee. "NeurASP: Embracing Neural Networks into Answer Set Programming." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/243.

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We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neural network in symbolic computation and how it can improve the neural network's perception result by applying symbolic reasoning in answer set programming. Also, NeurASP can make use of ASP rules to train a neural network better so that a neural network not only learns from implicit correlations from the data but also from the explicit complex semantic constraints expressed by the rules.

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Ahmed S, Abdulmalek, Salaheldin Elkatatny, Abdulwahab Z. Ali, Abdulazeez Abdulraheem, and Mohamed Mahmoud. "Artificial Neural Network ANN Approach to Predict Fracture Pressure." In SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers, 2019. http://dx.doi.org/10.2118/194852-ms.

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Farooque, Md Umar, Shufali A. Wani, and Shakeb A. Khan. "Artificial neural network (ANN) based implementation of Duval pentagon." In 2015 International Conference on Condition Assessment Techniques in Electrical Systems (CATCON). IEEE, 2015. http://dx.doi.org/10.1109/catcon.2015.7449506.

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Khan, Maleika Heenaye-Mamode. "Automated breast cancer diagnosis using artificial neural network (ANN)." In 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS). IEEE, 2017. http://dx.doi.org/10.1109/icspis.2017.8311589.

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Kumari, Neha, and Vani Bhargava. "Artificial Neural Network." In 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, 2019. http://dx.doi.org/10.1109/icict46931.2019.8977685.

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Galangque, Cherry Mae J., and Sherwin A. Guirnaldo. "Gunshot Classification and Localization System using Artificial Neural Network (ANN)." In 2019 12th International Conference on Information & Communication Technology and System (ICTS). IEEE, 2019. http://dx.doi.org/10.1109/icts.2019.8850937.

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Reports on the topic "ANN (Artificial Neural Network)"

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Vitela, J. E., U. R. Hanebutte, and J. Reifman. An artificial neural network controller for intelligent transportation systems applications. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/219376.

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Vela, Daniel. Forecasting latin-american yield curves: an artificial neural network approach. Banco de la República, 2013. http://dx.doi.org/10.32468/be.761.

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

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

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Huang, Wenrui, and Catherine Murray. Application of an Artificial Neural Network to Predict Tidal Currents in an Inlet. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada592255.

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Fitch, J. The radon transform for data reduction, line detection, and artificial neural network preprocessing. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6874873.

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Doherty, T. J. Use of An Adaptive Neural Network to Simulate Physiological Control Systems: Feasibility Study Using Artificial Systems. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada373572.

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He, L. M., L. L. Kear-Padilla, S. H. Lieberman, and J. M. Andrews. Online Monitoring of Oils in Wastewater Using Combined Ultraviolet Fluorescence and Light Scattering with an Artificial Neural Network. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada375432.

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Nikiforov, Vladimir. Smart Technical Systems of Measuring Technology and Measuring Technique, integrated into the smart complexes of medical technologies including laser Gears with the elements of Artificial Intelligence and Artificial neural network as form of Machine Learning. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2120.

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Powell, Bruce C. Artificial Neural Network Analysis System. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada392390.

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

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