Gotowe bibliografie tematyczne / Feed-forward ANNs

Gotowa bibliografia na temat „Feed-forward ANNs”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 20 lutego 2023

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Artykuły w czasopismach na temat "Feed-forward ANNs"

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Golpour, Iman, Ana Cristina Ferrão, Fernando Gonçalves, Paula M. R. Correia, Ana M. Blanco-Marigorta i Raquel P. F. Guiné. "Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)". Foods 10, nr 9 (20.09.2021): 2228. http://dx.doi.org/10.3390/foods10092228.

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This research study focuses on the evaluation of the total phenolic compounds (TPC) and antioxidant activity (AOA) of strawberries according to different experimental extraction conditions by applying the Artificial Neural Networks (ANNs) technique. The experimental data were applied to train ANNs using feed- and cascade-forward backpropagation models with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) algorithms. Three independent variables (solvent concentration, volume/mass ratio and extraction time) were used as ANN inputs, whereas the three variables of total phenolic compounds, DPPH and ABTS antioxidant activities were considered as ANN outputs. The results demonstrate that the best cascade- and feed-forward backpropagation topologies of ANNs for the prediction of total phenolic compounds and DPPH and ABTS antioxidant activity factors were the 3-9-1, 3-4-4-1 and 3-13-10-1 structures, with the training algorithms of trainlm, trainbr, trainlm and threshold functions of tansig-purelin, tansig-tansig-tansig and purelin-tansig-tansig, respectively. The best R2 values for the predication of total phenolic compounds and DPPH and ABTS antioxidant activity factors were 0.9806 (MSE = 0.0047), 0.9651 (MSE = 0.0035) and 0.9756 (MSE = 0.00286), respectively. According to the comparison of ANNs, the results showed that the cascade-forward backpropagation network showed better performance than the feed-forward backpropagation network for predicting the TPC, and the FFBP network, in predicting the DPPH and ABTS antioxidant activity factors, had more precision than the cascade-forward backpropagation network. The ANN technique is a potential method for estimating targeted total phenolic compounds and the antioxidant activity of strawberries.
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O'Reilly, G., C. C. Bezuidenhout i J. J. Bezuidenhout. "Artificial neural networks: applications in the drinking water sector". Water Supply 18, nr 6 (31.01.2018): 1869–87. http://dx.doi.org/10.2166/ws.2018.016.

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Abstract Artificial neural networks (ANNs) could be used in effective drinking water quality management. This review provides an overview about the history of ANNs and their applications and shortcomings in the drinking water sector. From the papers reviewed, it was found that ANNs might be useful modelling tools due to their successful application in areas such as pipes/infrastructure, membrane filtration, coagulation dosage, disinfection residuals, water quality, etc. The most popular ANNs applied were feed-forward networks, especially Multi-layer Perceptrons (MLPs). It was also noted that over the past decade (2006–2016), ANNs have been increasingly applied in the drinking water sector. This, however, is not the case for South Africa where the application of ANNs in distribution systems is little to non-existent. Future research should be directed towards the application of ANNs in South African distribution systems and to develop these models into decision-making tools that water purification facilities could implement.
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Daud, Suleman, Khan Shahzada, M. Tufail i M. Fahad. "Stream Flow Modeling of River Swat Using Regression and Artificial Neural Networks (ANNs) Techniques". Advanced Materials Research 255-260 (maj 2011): 679–83. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.679.

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This paper presents the utility of Artificial Neural Networks and Regression analysis for the stream flow modeling in Swat River at five discharge gauge station. As an appropriate intelligent model is identified, Artificial Neural Networks (ANNs) is evaluated by comparing it to regression analysis and the available field data. ANNs namely feed forward back propagation neural network (FFBPNN) and regression analysis are introduced and implemented. The research study successfully compared the performance of the ANN and regression models that validated the utility of the two modeling techniques as effective applications to stream flow forecasting.
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Novickis, Rihards, Daniels Jānis Justs, Kaspars Ozols i Modris Greitāns. "An Approach of Feed-Forward Neural Network Throughput-Optimized Implementation in FPGA". Electronics 9, nr 12 (18.12.2020): 2193. http://dx.doi.org/10.3390/electronics9122193.

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Artificial Neural Networks (ANNs) have become an accepted approach for a wide range of challenges. Meanwhile, the advancement of chip manufacturing processes is approaching saturation which calls for new computing solutions. This work presents a novel approach of an FPGA-based accelerator development for fully connected feed-forward neural networks (FFNNs). A specialized tool was developed to facilitate different implementations, which splits FFNN into elementary layers, allocates computational resources and generates high-level C++ description for high-level synthesis (HLS) tools. Various topologies are implemented and benchmarked, and a comparison with related work is provided. The proposed methodology is applied for the implementation of high-throughput virtual sensor.
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Al Khatib, Mohamed, i Samer Al Martini. "A Study on the Application of Artificial Neural Networks on Green Self Consolidating Concrete (SCC) under Hot Weather". Key Engineering Materials 677 (styczeń 2016): 254–59. http://dx.doi.org/10.4028/www.scientific.net/kem.677.254.

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Self-consolidating concrete (SCC) has recently drawn attention to the construction industry in hot weather countries, due to its high fresh and mechanical properties. The slump flow is routinely used for quality control of SCC. Experiments were conducted by the current authors to investigate the effects of hot weather conditions on the slump flow of SCC. Self-consolidating concrete mixtures were prepared with different dosages of fly ash and superplasticizer and under different ambient temperatures. The results showed that the slump flow of SCC is sensitive to changes in ambient temperature, fly ash dosage, and superplasticizer dosage. In this paper, several artificial neural networks (ANNs) were employed to predict the slump flow of self-consolidating concrete under hot weather. Some of the data used to construct the ANNs models in this paper were collected from the experimental study conducted by the current authors, and other data were gathered from literature. Various parameters including ambient temperature and mixing time were used as inputs during the construction of ANN models. The developed ANN models employed two neural networks: the Feed-Forward Back Propagation (FFBP) and the Cascade Forward Back Propagation (CFBP). Both FFBP and CFBP showed good predictability to the slump flow of SCC mixtures. However, the FFBP network showed a slight better performance than CFBP, where it better predicted the slump flow of SCC than the CFBP network under hot weather. The results in this paper indicate that the ANNs can be employed to help the concrete industry in hot weather to predict the quality of fresh self-consolidating concrete mixes without the need to go through long trial and error testing program.Keywords: Self-consolidating concrete; Neural networks; Hot weather, Feed-forward back-propagation, Cascade-forward back propagation.
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Sabir, Zulqurnain, Thongchai Botmart, Muhammad Asif Zahoor Raja, Wajaree Weera i Fevzi Erdoğan. "A stochastic numerical approach for a class of singular singularly perturbed system". PLOS ONE 17, nr 11 (28.11.2022): e0277291. http://dx.doi.org/10.1371/journal.pone.0277291.

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In the present study, a neuro-evolutionary scheme is presented for solving a class of singular singularly perturbed boundary value problems (SSP-BVPs) by manipulating the strength of feed-forward artificial neural networks (ANNs), global search particle swarm optimization (PSO) and local search interior-point algorithm (IPA), i.e., ANNs-PSO-IPA. An error-based fitness function is designed using the differential form of the SSP-BVPs and its boundary conditions. The optimization of this fitness function is performed by using the computing capabilities of ANNs-PSO-IPA. Four cases of two SSP systems are tested to confirm the performance of the suggested ANNs-PSO-IPA. The correctness of the scheme is observed by using the comparison of the proposed and the exact solutions. The performance indices through different statistical operators are also provided to solve the SSP-BVPs using the proposed ANNs-PSO-IPA. Moreover, the reliability of the scheme is observed by taking hundred independent executions and different statistical performances have been provided for solving the SSP-BVPs to check the convergence, robustness and accuracy.
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Mahmoudi, Amir Hossein, Mitra Ghanbari-Matloob i Soroush Heydarian. "A Neural Networks Approach to Measure Residual Stresses Using Spherical Indentation". Materials Science Forum 768-769 (wrzesień 2013): 114–19. http://dx.doi.org/10.4028/www.scientific.net/msf.768-769.114.

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In the present study an Artificial Neural Network (ANN) approach is proposed for residual stresses estimation in engineering components using indentation technique. First of all, load-penetration curves of indentation tests for tensile and compressive residual stresses are studied using Finite Element Method (FEM) for materials with different yield stresses and work-hardening exponents. Then, experimental tests are carried out on samples made of 316L steel without residual stresses. In the next step, multi-layer feed forward ANNs are created and trained based on 80% of obtained numerical data using Back-Error Propagation (BEP) algorithm. Then the trained ANNs are tested against the remaining data. The obtained results show that the predicted residual stresses are in good agreement with the actual data.
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Kaveh, M., i R. A. Chayjan. "Mathematical and neural network modelling of terebinth fruit under fluidized bed drying". Research in Agricultural Engineering 61, No. 2 (2.06.2016): 55–65. http://dx.doi.org/10.17221/56/2013-rae.

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The paper presents an application which uses Feed Forward Neural Networks (FFNNs) to model the non-linear behaviour of the terebinth fruit drying. Mathematical models and Artificial Neural Networks (ANNs) were used for prediction of effective moisture diffusivity, specific energy consumption, shrinkage, drying rate and moisture ratio in terebinth fruit. Feed Forward Neural Network (FFBP) and Cascade Forward Neural Network (CFNN) as well as training algorithms of Levenberg-Marquardt (LM) and Bayesian regularization (BR) were used. Air temperature and velocity limits were 40–80°C and 0.81–4.35 m/s, respectively. The best outcome for the use of ANN for the effective moisture diffusivity appertained to CFNN network with BR training algorithm, topology of 2-3-1 and threshold function of TANSIG. Similarly, the best outcome for the use of ANN for drying rate and moisture ratio also appertained to CFNN network with LM training algorithm, topology of 3-2-4-2 and threshold function of TANSIG.
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Abujayyab, S. K. M., M. A. S. Ahamad, A. S. Yahya i A. M. H. Y. Saad. "A NEW FRAMEWORK FOR GEOSPATIAL SITE SELECTION USING ARTIFICIAL NEURAL NETWORKS AS DECISION RULES: A CASE STUDY ON LANDFILL SITES". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2/W2 (19.10.2015): 131–38. http://dx.doi.org/10.5194/isprsannals-ii-2-w2-131-2015.

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This paper briefly introduced the theory and framework of geospatial site selection (GSS) and discussed the application and framework of artificial neural networks (ANNs). The related literature on the use of ANNs as decision rules in GSS is scarce from 2000 till 2015. As this study found, ANNs are not only adaptable to dynamic changes but also capable of improving the objectivity of acquisition in GSS, reducing time consumption, and providing high validation. ANNs make for a powerful tool for solving geospatial decision-making problems by enabling geospatial decision makers to implement their constraints and imprecise concepts. This tool offers a way to represent and handle uncertainty. Specifically, ANNs are decision rules implemented to enhance conventional GSS frameworks. The main assumption in implementing ANNs in GSS is that the current characteristics of existing sites are indicative of the degree of suitability of new locations with similar characteristics. GSS requires several input criteria that embody specific requirements and the desired site characteristics, which could contribute to geospatial sites. In this study, the proposed framework consists of four stages for implementing ANNs in GSS. A multilayer feed-forward network with a backpropagation algorithm was used to train the networks from prior sites to assess, generalize, and evaluate the outputs on the basis of the inputs for the new sites. Two metrics, namely, confusion matrix and receiver operating characteristic tests, were utilized to achieve high accuracy and validation. Results proved that ANNs provide reasonable and efficient results as an accurate and inexpensive quantitative technique for GSS.
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Gallo, Mariano, i Giuseppina De Luca. "Spatial Extension of Road Traffic Sensor Data with Artificial Neural Networks". Sensors 18, nr 8 (12.08.2018): 2640. http://dx.doi.org/10.3390/s18082640.

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This paper proposes a method for estimating traffic flows on some links of a road network knowing the data on other links that are monitored with sensors. In this way, it is possible to obtain more information on traffic conditions without increasing the number of monitored links. The proposed method is based on artificial neural networks (ANNs), wherein the input data are the traffic flows on some monitored road links and the output data are the traffic flows on some unmonitored links. We have implemented and tested several single-layer feed-forward ANNs that differ in the number of neurons and the method of generating datasets for training. The proposed ANNs were trained with a supervised learning approach where input and output example datasets were generated through traffic simulation techniques. The proposed method was tested on a real-scale network and gave very good results if the travel demand patterns were known and used for generating example datasets, and promising results if the demand patterns were not considered in the procedure. Numerical results have underlined that the ANNs with few neurons were more effective than the ones with many neurons in this specific problem.
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Rozprawy doktorskie na temat "Feed-forward ANNs"

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

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

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Last few decades have witnessed the use of artificial neural networks (ANN) in many real-world applications and have offered an attractive paradigm for a broad range of adaptive complex systems. In recent years ANN have enjoyed a great deal of success and have proven useful in wide variety pattern recognition or feature extraction tasks. Examples include optical character recognition, speech recognition and adaptive control to name a few. To keep the pace with its huge demand in diversified application areas, many different kinds of ANN architecture and learning types have been proposed by the researchers to meet varying needs. A novel hybrid learning approach for the training of a feed-forward ANN has been proposed in this thesis. The approach combines evolutionary algorithms with matrix solution methods such as singular value decomposition, Gram-Schmidt etc., to achieve optimum weights for hidden and output layers. The proposed hybrid method is to apply evolutionary algorithm in the first layer and least square method (LS) in the second layer of the ANN. The methodology also finds optimum number of hidden neurons using a hierarchical combination methodology structure for weights and architecture. A learning algorithm has many facets that can make a learning algorithm good for a particular application area. Often there are trade offs between classification accuracy and time complexity, nevertheless, the problem of memory complexity remains. This research explores all the different facets of the proposed new algorithm in terms of classification accuracy, convergence property, generalization ability, time and memory complexity.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information Technology
Full Text
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Svärd, Simon. "ANN som en metod för att göra urval i spel". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13673.

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I detta arbete som fokuserar på hur neurala nätverk kan appliceras på och hur väl de presterar i en spelmiljö undersöks två nätverksarkitekturer applicerat på en simulation av ett så kallat urvalsbaserat spel. I arbetet så är ett urvalsbaserat spel ett spel som går ut på att en spelare skall göra en mängd val innan spelet börjar, och de två nätverksarkitekturerna som utvärderas är Feed Forward och NEAT. Experimenten låter nätverken skapa lag för en förenklad version av spelet Pokemon och kommer sedan att låta dessa lag tävla emot varandra i en deterministisk testmiljö för att bedöma hur bra nätverken presterar.
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Nigrini, L. B., i G. D. Jordaan. "Short term load forecasting using neural networks". Journal for New Generation Sciences, Vol 11, Issue 3: Central University of Technology, Free State, Bloemfontein, 2013. http://hdl.handle.net/11462/646.

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Published Article
Several forecasting models are available for research in predicting the shape of electric load curves. The development of Artificial Intelligence (AI), especially Artificial Neural Networks (ANN), can be applied to model short term load forecasting. Because of their input-output mapping ability, ANN's are well-suited for load forecasting applications. ANN's have been used extensively as time series predictors; these can include feed-forward networks that make use of a sliding window over the input data sequence. Using a combination of a time series and a neural network prediction method, the past events of the load data can be explored and used to train a neural network to predict the next load point. In this study, an investigation into the use of ANN's for short term load forecasting for Bloemfontein, Free State has been conducted with the MATLAB Neural Network Toolbox where ANN capabilities in load forecasting, with the use of only load history as input values, are demonstrated.
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Přecechtěl, Roman. "Optimalizace řízení aktivního síťového prvku". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218166.

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The thesis deals with the use of neuronal networks for the control of telecommunication network elements. The aim of the thesis is to create a simulation model of network element with switching array with memory, in which the optimization kontrol switching array is solved by means of the neural network. All source code is created in integrated environment MATLAB. To training are used feed-forward backpropagation network. Miss achieve satisfactory result mistakes. Work apposite decision procedure given to problem and it is possible on ni tie up in an effort to find optimum solving.
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Gosal, Gurpreet Singh. "The use of Inverse Neural Networks in the Fast Design of Printed Lens Antennas". Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32249.

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In this thesis the major objective is the implementation of the inverse neural network concept in the design of printed lens (transmitarray) antenna. As it is computationally extensive to perform full-wave simulations for entire transmitarray structure and thereafter perform optimization, the idea is to generate a design database assuming that a unit cell of the transmitarray is situated inside a 2D infinite periodic structure. This way we generate a design database of transmission coefficient by varying the unit cell parameters. Since, for the actual design, we need dimensions for each cell on the transmitarray aperture and to do this we need to invert the design database. The major contribution of this thesis is the proposal and the implementation of database inversion methodology namely inverse neural network modelling. We provide the algorithms for carrying out the inversion process as well as provide check results to demonstrate the reliability of the proposed methodology. Finally, we apply this approach to design a transmitarray antenna, and measure its performance.
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Części książek na temat "Feed-forward ANNs"

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Dorado, Julian, Juan R. Rabuñal, Antonino Santos, Alejandro Pazos i Daniel Rivero. "Automatic Recurrent and Feed-Forward ANN Rule and Expression Extraction with Genetic Programming". W Parallel Problem Solving from Nature — PPSN VII, 485–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45712-7_47.

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Singh, Poornima, Vinod Kumar Singh, Archana Lala i Akash Kumar Bhoi. "Design and Analysis of Microstrip Antenna Using Multilayer Feed-Forward Back-Propagation Neural Network (MLPFFBP-ANN)". W Advances in Communication, Devices and Networking, 393–98. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7901-6_43.

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Yucel, Melda, Sinan Melih Nigdeli i Gebrail Bekdaş. "Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems". W Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering, 13–38. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0301-0.ch002.

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This chapter reveals the advantages of artificial neural networks (ANNs) by means of prediction success and effects on solutions for various problems. With this aim, initially, multilayer ANNs and their structural properties are explained. Then, feed-forward ANNs and a type of training algorithm called back-propagation, which was benefited for these type networks, are presented. Different structural design problems from civil engineering are optimized, and handled intended for obtaining prediction results thanks to usage of ANNs.
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Kakkar, Deepti, i Ashish Raman. "Human-Machine Interface-Based Robotic Wheel Chair Control". W Futuristic Design and Intelligent Computational Techniques in Neuroscience and Neuroengineering, 1–22. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7433-1.ch001.

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This chapter presents the P300-based human machine interference (HMI) systems control robotic wheel chair (RWC) prototype in right, left, forward, backward, and stop positions. Four different targets letters are used to elicit the P300 waves, flickering in the low frequency region, by using oddball paradigms and displayed on a liquid crystal display (LCD) screen by Lab-VIEW. After the pre-processing and taking one second time window, feature is extracted by using discrete wavelet transform (DWT). Three different classifiers—two based on ANNs pattern recognition neural network (PRNN) and feed forward neural network (FFNN) and the and other one based on support vector machine (SVM)—are used. Those three techniques are designed and compared with the different accuracies among them.
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Sarma, Kandarpa Kumar. "Learning Aided Digital Image Compression Technique for Medical Application". W Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing, 400–422. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8654-0.ch019.

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The explosive growths in data exchanges have necessitated the development of new methods of image compression including use of learning based techniques. The learning based systems aids proper compression and retrieval of the image segments. Learning systems like. Artificial Neural Networks (ANN) have established their efficiency and reliability in achieving image compression. In this work, two approaches to use ANNs in Feed Forward (FF) form and another based on Self Organizing Feature Map (SOFM) is proposed for digital image compression. The image to be compressed is first decomposed into smaller blocks and passed to FFANN and SOFM networks for generation of codebooks. The compressed images are reconstructed using a composite block formed by a FFANN and a Discrete Cosine Transform (DCT) based compression-decompression system. Mean Square Error (MSE), Compression ratio (CR) and Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of the system.
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Kumar, K. Vinoth, i Prawin Angel Michael. "Detection of Stator and Rotor Faults in Asynchronous Motor Using Artificial Intelligence Method". W Advances in Computer and Electrical Engineering, 278–85. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3531-7.ch013.

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This chapter deals with the implementation of a PC-based monitoring and fault identification scheme for a three-phase induction motor using artificial neural networks (ANNs). To accomplish the task, a hardware system is designed and built to acquire three phase voltages and currents from a 3.3KW squirrel-cage, three-phase induction motor. A software program is written to read the voltages and currents, which are first used to train a feed-forward neural network structure. The trained network is placed in a Lab VIEW-based program formula node that monitors the voltages and currents online and displays the fault conditions and turns the motor. The complete system is successfully tested in real time by creating different faults on the motor.
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Kumar, K. Vinoth, Ramya K. C. i Muhammad Irfan. "Advanced Fault Diagnosis Monitoring Scheme in Asynchronous Motor Using Soft Computing Method". W Advances in Computer and Electrical Engineering, 89–97. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6989-3.ch004.

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This chapter deals with the implementation of a PC-based monitoring and fault identification scheme for a three-phase induction motor using artificial neural networks (ANNs). To accomplish the task, a hardware system is designed and built to acquire three phase voltages and currents from a 3.3KW squirrel-cage, three-phase induction motor. A software program is written to read the voltages and currents, which are first used to train a feed-forward neural network structure. The trained network is placed in a Lab VIEW-based program formula node that monitors the voltages and currents online and displays the fault conditions and turns the motor. The complete system is successfully tested in real time by creating different faults on the motor.
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Emara, Tamer. "Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network". W Research Anthology on Artificial Neural Network Applications, 471–89. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch022.

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The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.
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Gurjar, Arunaben Prahladbhai, i Shitalben Bhagubhai Patel. "Fundamental Categories of Artificial Neural Networks". W Research Anthology on Artificial Neural Network Applications, 1–30. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch001.

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The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.
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Gurjar, Arunaben Prahladbhai, i Shitalben Bhagubhai Patel. "Fundamental Categories of Artificial Neural Networks". W Applications of Artificial Neural Networks for Nonlinear Data, 30–64. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4042-8.ch003.

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The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.
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Streszczenia konferencji na temat "Feed-forward ANNs"

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Rivero, Daniel, Julian Dorado, Juan Rabunal i Alejandro Pazos. "Evolving simple feed-forward and recurrent ANNs for signal classification: A comparison". W 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178621.

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Zinati, Reza Farshbaf, i Mohammad Reza Razfar. "Constrained Optimization of Surface Roughness in Longitudinal Turning via Novel Modified Harmony Search". W ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50005.

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The present research deals with a modified optimization algorithm of harmony search coupled with artificial neural networks (ANNs) to predict the optimal cutting condition. To this end, several experiments were carried out on AISI 1045 steel to attain required data for training of ANNs. Feed forward artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting experimental data, and Modified Harmony Search algorithm (MHS) was used to find the constrained optimum of the surface roughness. Furthermore, Simple Harmony Search algorithm (SHS) and Genetic Algorithm (GA) were used for solving the same optimization problem to illustrate the capabilities of MHS algorithm. The obtained results demonstrate that MHS algorithm is more effective and authoritative in approaching the global solution than the SHS algorithm and GA.
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Olausson, Pernilla, Daniel Ha¨ggsta˚hl, Jaime Arriagada, Erik Dahlquist i Mohsen Assadi. "Hybrid Model of an Evaporative Gas Turbine Power Plant Utilizing Physical Models and Artificial Neural Networks". W ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38116.

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Traditionally, when process identification, monitoring and diagnostics are carried out for power plants and engines, physical modeling such as heat and mass balances, gas path analysis, etc. is utilized to keep track of the process. This type of modeling both requires and provides considerable knowledge of the process. However, if high accuracy of the model is required, this is achieved at the expense of computational time. By introducing statistical methods such as Artificial Neural Networks (ANNs), the accuracy of the complex model can be maintained while the calculation time is often reduced significantly reduced. The ANN method has proven to be a fast and reliable tool for process identification, but the step from the traditional physical model to a pure ANN model is perhaps too wide and, in some cases, perhaps unnecessary also. In this work, the Evaporative Gas Turbine (EvGT) plant was modeled using both physical relationships and ANNs, to end up with a hybrid model. The type of architecture used for the ANNs was the feed-forward, multi-layer neural network. The main objective of this study was to evaluate the viability, the benefits and the drawbacks of this hybrid model compared to the traditional approach. The results of the case study have clearly shown that the hybrid model is preferable. Both the traditional and the hybrid models have been verified using measured data from an existing pilot plant. The case study also shows the simplicity of integrating an ANN into conventional heat and mass balance software, already implemented in many control systems for power plants. The access to a reliable and faster hybrid model will ultimately give more reliable operation, and ultimately the lifetime profitability of the plant will be increased. It is also worth mentioning that for diagnostic purposes, where advanced modeling is important, the hybrid model with calculation time well below one second could be used to advantage in model predictive control (MPC).
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Ravindranath, G., G. P. Prabhukumar i B. Channamalla Devaru. "Application of an Artificial Neural Network in Gas-Solid (Air-Solid) Fluidized Bed: Heat Transfer Predictions". W ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42881.

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This paper presents heat transfer analysis of in-line arrangement of bare tube bundles in gas-solid (air-solid) fluidized bed and predictions are done by using Artificial Neural Network (ANN) based on the experimental data. Measurement of average heat transfer coefficient was made by local thermal simulation technique in a cold square bubbling air-fluidized bed of size 0.305m × 0.305m. Studies were conducted for bare tube bundles of in -line arrangement using beds of small (average particle diameter less than 1mm) silica sand particles and of large (average particle diameter greater than 1mm) particle (raagi and mustard). Within the range of experimental conditions influence of bed particle diameter (Dp), fluidizing velocity (U) were studied, which are significant parameters affecting heat transfer. Artificial neural networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, and non-linearity. Here, feed-forward architecture and trained by back-propagation technique is adopted to predict heat transfer analysis found from experimental results. The ANN is designed to suit the present system which has 3 inputs and 2 outputs. The network predictions are found to be in very good agreement with the experimental observed values of bare tube heat transfer coefficient (hb) and Nusselt number of bare tube (Nub).
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Govindaswamy, Ravindranath, i Savitha Srinivasan. "Application of Artificial Neural Network for Single Horizontal Bare Tube and Bare Tube Bundles in Gas-Solid (Air-Solid) Fluidized Bed of Small and Large Particles: Heat Transfer Predictions". W ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-66265.

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This paper presents heat transfer analysis of single horizontal bare tube and in-line arrangement of bare tube bundles in gas-solid (air-solid) fluidized bed and predictions are done by using Artificial Neural Network (ANN) based on the experimental data. Measurement of average heat transfer coefficient was made by local thermal simulation technique in a cold square bubbling air-fluidized bed of size 0.305m × 0.305m. Studies were conducted for single bare tube and bare tube bundles of in–line arrangement using beds of small (average particle diameter less than 1mm) silica sand particles and of large (average particle diameter greater than 1mm) particle (raagi and mustard). Within the range of experimental conditions influence of bed particle diameter (Dp), fluidizing velocity (U) were studied, which are significant parameters affecting heat transfer. Artificial neural networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, and non-linearity. Here, feed-forward architecture and trained by back-propagation technique is adopted to predict heat transfer analysis found from experimental results. The ANN is designed to suit the present system which has 3 inputs and 2 outputs. The network predictions are found to be in very good agreement with the experimental observed values of bare tube heat transfer coefficient (hb) and Nusselt number of bare tube (Nub).
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Chong, Zyh Siong, Steven Wilcox i John Ward. "The Use of Artificial Intelligence in the Modelling and Heat Treatment Parameters Identification for Alloy-Steel Re-Heating Process". W ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84802.

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This paper describes the work undertaken by the University of Glamorgan and CORUS Rotherham UK to apply artificial neural networks to model the cold alloy-steel bars and the heat treatment parameters with their end-product quality characteristics. Standard multi-layered feed forward artificial neural networks (ANNs) were employed to represent the functional mapping of inputs such as physical dimension, material composition and the parameters of the heat treatment cycles to the Brinell Hardness (HB) and the Ultimate Tensile Strength (UTS). The HB and UTS networks were validated with new data sets and demonstrated a satisfactory level of predictions over a range of conditions. These neural networks were then integrated into a Genetic Algorithm (GA) search strategy to identify the best material characteristics and furnace operating parameters in order that both the HB and UTS values are maximised. The results demonstrated that the hybrid strategy of combining the neural network based models with GA can deliver sensible results.
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Shi, Yunye, Diego Yepes Maya i Albert Ratner. "Predicting Steam-Gasification Output Using Artificial Neural Networks". W ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-71635.

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Abstract Steam gasification has been identified to be one of the most important technologies to produce syngas for biofuel synthesis, due to the high H2 concentration it can reach, which is significantly higher compared with conventional air gasification processes. Double-stage downdraft gasification has proven to be efficient with a secondary steam injection. Various kinds of models, including computational fluid dynamics (CFD), thermo-equilibrium, kinetic rate, and artificial neural network (ANN) have been developed for studying these processes. Equilibrium models assume of chemical reaction equilibrium, in which the transfer phenomenon and the reaction kinetics of primary reactions are considered. In CFD models, the mass, momentum, and energy equations are solved simultaneously along the consideration of the gasifier design to predict parameters such as temperature or species concentrations. To overcome the limitations posed by these models, artificial intelligence tools such as ANNs are reliable for the prediction of nonlinear system data. The application of ANN in gasification processes have been reported by several researchers. ANN based models have been developed for predicting syngas yields and gas composition in air gasification systems. However, to the author’s knowledge, no study has focused in the prediction, using ANN, of a double-stage steam gasification system. In the current study, the effect of different temperatures along the gasification process (T0-T7), equivalence ratio, and steam-to-air ratio were studied in an artificial neural network model to predict the gas composition (CO, CH4, and H2) and the lower heating value (LHV) of the syngas. Miscanthus grass is selected as the fuel. The ultimate analysis of the fuel is excluded in the input because the insignificant impact on syngas composition especially H2 production. Feed forward back propagation (FFBP) neural networks were employed for the training of the networks. Then the model with optimal parameters were selected to test against experimental data for validation purposes. Further, the unused data are tested for model accuracy. It was observed that the developed models are able to predict the CO, CH4, H2, and LHV with good accuracy when comparing with experimental data. The FFBP showed excellent performance. A higher coefficient of determination (R2) and lower mean square error (MSE) is achieved by FFBP. The results obtained indicate that ANNs are powerful tools to facilitate system design, operation and control of double-stage downdraft gasifiers with various operating conditions. Utilizing ANN models, one can predict reasonable output parameters without unnecessary failure.
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Viano, Andrea, Gabriele Ottino, Luca Ratto i Giuseppe Spataro. "Coupled CFD-ANN Procedure for Extending Heat Transfer Correlations Out of Their Range of Validity". W ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-69707.

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The heat transfer coefficient and pressure losses are among the main parameters to be evaluated in gas turbine cooling network design. Due to the complexity of these estimates, correlation-based computations are typically used as a result of time-consuming and expensive experimental activities. One of the main problems that the industry has to face is that these correlations, based on non-dimensional experimental data, produce reliable results in a range of validity typically different from that encountered in gas turbine applications. This paper will present preliminary results of an innovative procedure based on CFD analyses and Artificial Neural Networks, able to extend correlation predictions out of their range of validity, without any additional experimental data. Well-known test cases were replicated by building corresponding CAD geometries which were discretized by means of appropriate meshes, resulting from grid-independence studies. CFD analyses, based on the RANS approach, were performed to overlay the computations of the Nusselt number obtained from experimental activities. A preliminary comparison among turbulence models was carried out to find one leading to a good agreement with the experimental data. Then, an optimization method, based on Evolutionary Algorithms, was applied to the CFD analyses in order to find the best set of constant values for the chosen turbulence model, leading to the most accurate prediction of the experimental dataset. The resulting ad hoc CFD model was adopted in order to analyse test case configurations characterized by parameters within and external to the correlation validity field, building a sufficiently wide feeding database. A feed-forward multi-layer neural network was selected among network architectures typically used in engineering applications for prediction analyses. ANNs were chosen because they enable the solution of these complex nonlinear problems by using simple computational operations. The selected Artificial Neural Network was trained by a back-propagation procedure on the CFD results regarding Nusselt number. The validation of the resulting ANN was performed comparing its outputs with experimental data external to the correlation range of validity, which had not been used in the training session. Good agreement has been found. Results are presented and discussed.
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Ovcharenko, O., V. Kazei, D. Peter, X. Zhang i T. Alkhalifah. "Low-Frequency Data Extrapolation Using a Feed-Forward ANN". W 80th EAGE Conference and Exhibition 2018. Netherlands: EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201801231.

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Kumar, Narander, i Pooja Patel. "Resource Management using Feed Forward ANN-PSO in Cloud Computing Environment". W the Second International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2905055.2905115.

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Raporty organizacyjne na temat "Feed-forward ANNs"

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

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