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Gotowe bibliografie tematyczne / BACK PROPAGATION ALGORITHM (BPA)

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Gotowa bibliografia na temat „BACK PROPAGATION ALGORITHM (BPA)”

Autor: Grafiati

Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "BACK PROPAGATION ALGORITHM (BPA)"

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R., Bhuvana, Purushothaman S., Rajeswari R., and Balaji R.G. "Development of combined back propagation algorithm and radial basis function for diagnosing depression patients." International Journal of Engineering & Technology 4, no. 1 (2015): 244. http://dx.doi.org/10.14419/ijet.v4i1.4201.

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Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA) and Radial Basis Function (RBF) are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN) and one desired categ

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Khanum, Afshan, S. Purushothaman, and P. Rajeswari. "Performance comparisons of the soft computing algorithms in lung segmentation and nodule identification." International Journal of Engineering & Technology 7, no. 1.1 (2017): 189. http://dx.doi.org/10.14419/ijet.v7i1.1.9287.

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This paper presents the implementation back propagation algorithm (BPA) and fuzzy logic(FL) in lung image segmentation and nodule identification. Lung image database consortium (LIDC) database images has been used. Features are extracted using statistical methods. These features are used for training the BPA and FL algorithms. Weights are stored in a file that is used for segmentation of the lung image. Subsequently, texture properties are used for nodule identification.

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Al-Araji, Ahmed Sabah, and Shaymaa Jafe'er Al-Zangana. "Design of New Hybrid Neural Controller for Nonlinear CSTR System based on Identification." Journal of Engineering 25, no. 4 (2019): 70–89. http://dx.doi.org/10.31026/j.eng.2019.04.06.

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This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the

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Sujatha, K., N. Pappa, U. Siddharth Nambi, C. R. Raja Dinakaran, and K. Senthil Kumar. "Intelligent Parallel Networks for Combustion Quality Monitoring in Power Station Boilers." Advanced Materials Research 699 (May 2013): 893–99. http://dx.doi.org/10.4028/www.scientific.net/amr.699.893.

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This research work includes a combination of Fisher’s Linear Discriminant (FLD) analysis by combining Radial Basis Function Network (RBF) and Back Propagation Algorithm (BPA) for monitoring the combustion conditions of a coal fired boiler so as to control the air/fuel ratio. For this two dimensional flame images are required which was captured with CCD camera whose features of the images, average intensity, area, brightness and orientation etc., of the flame are extracted after pre-processing the images. The FLD is applied to reduce the n-dimensional feature size to 2 dimensional feature size

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Song, Shaoqiu, Jie Lu, Shiqi Xing, et al. "Near Field 3-D Millimeter-Wave SAR Image Enhancement and Detection with Application of Antenna Pattern Compensation." Sensors 22, no. 12 (2022): 4509. http://dx.doi.org/10.3390/s22124509.

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In this paper, a novel near-field high-resolution image focusing technique is proposed. With the emergence of Millimeter-wave (mmWave) devices, near-field synthetic aperture radar (SAR) imaging is widely used in automotive-mounted SAR imaging, UAV imaging, concealed threat detection, etc. Current research is mainly confined to the laboratory environment, thus ignoring the adverse effects of the non-ideal experimental environment on imaging and subsequent detection in real scenarios. To address this problem, we propose an optimized Back-Projection Algorithm (BPA) that considers the loss path of

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Vinay, Kumar Jain. "A comparative analysis of neural network function: resilient back propagation algorithm (BPA) and radial basis functions (RBF) in multilingual environment." i-manager's Journal on Digital Signal Processing 10, no. 1 (2022): 9. http://dx.doi.org/10.26634/jdp.10.1.18639.

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The most convenient speech processing tool is Artificial Neural Networks (ANNs). The effectiveness has been tested with various real-time applications. The classifier using artificial neural networks identifies utterances based on features extracted from the speech signal. The proposed approach to multilingual speaker identification consists of two parts, such as a training part and a testing part. In the training part, the classifier is trained using speech feature vectors. The spoken language contains complete information, such as details about the content of the message and details about th

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EMAM, NAMEER N. EL, and RASHEED ABDUL SHAHEED. "COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION." International Journal on Artificial Intelligence Tools 17, no. 06 (2008): 1089–108. http://dx.doi.org/10.1142/s021821300800431x.

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A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate

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Tayfour Ahmed, Amira, Altahir Mohammed, and Moawia Yahia. "Performance comparisons of artificial neural network algorithms in facial expression recognition." International Journal of Engineering & Technology 4, no. 4 (2015): 465. http://dx.doi.org/10.14419/ijet.v4i4.5069.

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This paper presents methods for identifying facial expressions. The objective of this paper is to present a combination of texture oriented method with dimensional reduction and use for training the Single-Layer Neural Network (SLN), Back Propagation Algorithm (BPA) and Cerebellar Model Articulation Controller (CMAC) for identifying facial expressions. The proposed methods are called intelligent methods that can accommodate for the variations in the facial expressions and hence prove to be better for untrained facial expressions. Conventional methods have limitations that facial expressions sh

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Venkaiah, Chintham, and Mallesham Dulla. "Static security based available transfer capability (ATC) computation for real-time power markets." Serbian Journal of Electrical Engineering 7, no. 2 (2010): 269–89. http://dx.doi.org/10.2298/sjee1002269v.

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In power system deregulation, the Independent System Operator (ISO) has the responsibility to control the power transactions and avoid overloading of the transmission lines beyond their thermal limits. To achieve this, the ISO has to update in real-time periodically Available Transfer Capability (ATC) index for enabling market participants to reserve the transmission service. In this paper Static Security based ATC has been computed for real-time applications using three artificial intelligent methods viz.: i) Back Propagation Algorithm (BPA); ii) Radial Basis Function (RBF) Neural network; an

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Le, Duc Van. "APPLICABILITY OF ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATION OF MONTHLY RUNOFF IN COMPARISON WITH SOM OTHER TRADITIONAL MODELS." Science and Technology Development Journal 12, no. 4 (2009): 94–106. http://dx.doi.org/10.32508/stdj.v12i4.2237.

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Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for fore

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Rozprawy doktorskie na temat "BACK PROPAGATION ALGORITHM (BPA)"

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Lowton, Andrew D. "A constructive learning algorithm based on back-propagation." Thesis, Aston University, 1995. http://publications.aston.ac.uk/10663/.

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There are been a resurgence of interest in the neural networks field in recent years, provoked in part by the discovery of the properties of multi-layer networks. This interest has in turn raised questions about the possibility of making neural network behaviour more adaptive by automating some of the processes involved. Prior to these particular questions, the process of determining the parameters and network architecture required to solve a given problem had been a time consuming activity. A number of researchers have attempted to address these issues by automating these processes, concentra

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Xiao, Nancy Y. (Nancy Ying). "Using the modified back-propagation algorithm to perform automated downlink analysis." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/40206.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.<br>Includes bibliographical references (p. 121-122).<br>by Nancy Y. Xiao.<br>M.Eng.

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Sargelis, Kęstas. "Klaidos skleidimo atgal algoritmo tyrimai." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2009~D_20090630_094557-88383.

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Šiame darbe detaliai išanalizuotas klaidos skleidimo atgal algoritmas, atlikti tyrimai. Išsamiai analizuota neuroninių tinklų teorija. Algoritmui taikyti ir analizuoti sistemoje Visual Studio Web Developer 2008 sukurta programa su įvairiais tyrimo metodais, padedančiais ištirti algoritmo daromą klaidą. Taip pat naudotasi Matlab 7.1 sistemos įrankiais neuroniniams tinklams apmokyti. Tyrimo metu analizuotas daugiasluoksnis dirbtinis neuroninis tinklas su vienu paslėptu sluoksniu. Tyrimams naudoti gėlių irisų ir oro taršos duomenys. Atlikti gautų rezultatų palyginimai.<br>The present work provide

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Albarakati, Noor. "FAST NEURAL NETWORK ALGORITHM FOR SOLVING CLASSIFICATION TASKS." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2740.

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Classification is one-out-of several applications in the neural network (NN) world. Multilayer perceptron (MLP) is the common neural network architecture which is used for classification tasks. It is famous for its error back propagation (EBP) algorithm, which opened the new way for solving classification problems given a set of empirical data. In the thesis, we performed experiments by using three different NN structures in order to find the best MLP neural network structure for performing the nonlinear classification of multiclass data sets. A developed learning algorithm used here is the ba

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Civelek, Ferda N. (Ferda Nur). "Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278824/.

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Representing time has been considered a general problem for artificial intelligence research for many years. More recently, the question of representing time has become increasingly important in representing human decision making process through connectionist expert systems. Because most human behaviors unfold over time, any attempt to represent expert performance, without considering its temporal nature, can often lead to incorrect results. A temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems, h

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Sisman, Yilmaz Nuran Arzu. "A Temporal Neuro-fuzzy Approach For Time Series Analysis." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/570366/index.pdf.

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The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may constitute the rule-base in a fuzzy expert system. Second, a temporal neuro-fuzzy system

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Guan, Xing. "Predict Next Location of Users using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263620.

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Predicting the next location of a user has been interesting for both academia and industry. Applications like location-based advertising, traffic planning, intelligent resource allocation as well as in recommendation services are some of the problems that many are interested in solving. Along with the technological advancement and the widespread usage of electronic devices, many location-based records are created. Today, deep learning framework has successfully surpassed many conventional methods in many learning tasks, most known in the areas of image and voice recognition. One of the neural

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Halabian, Faezeh. "An Enhanced Learning for Restricted Hopfield Networks." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42271.

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This research investigates developing a training method for Restricted Hopfield Network (RHN) which is a subcategory of Hopfield Networks. Hopfield Networks are recurrent neural networks proposed in 1982 by John Hopfield. They are useful for different applications such as pattern restoration, pattern completion/generalization, and pattern association. In this study, we propose an enhanced training method for RHN which not only improves the convergence of the training sub-routine, but also is shown to enhance the learning capability of the network. Particularly, after describing the architectur

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Cheng, Martin Chun-Sheng, and pjcheng@ozemail com au. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm." Griffith University. School of Microelectronic Engineering, 2003. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20030722.172812.

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Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. A general T2FNN is computational intensive due to the complexity of type 2 to type 1 reduction. Therefore the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of inte

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Cheng, Martin Chun-Sheng. "Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic Algorithm." Thesis, Griffith University, 2003. http://hdl.handle.net/10072/366350.

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Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. A general T2FNN is computational intensive due to the complexity of type 2 to type 1 reduction. Therefore the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of inte

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Książki na temat "BACK PROPAGATION ALGORITHM (BPA)"

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Lowton, Andrew David. A constructive learning algorithm based on back-propagation. Aston University. Department ofComputer Science and Applied Mathematics, 1995.

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Intelligent information retrieval using an inductive learning algorithm and a back-propagation neural network. University Microfilms International, 1995.

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Części książek na temat "BACK PROPAGATION ALGORITHM (BPA)"

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Lopes, Noel, and Bernardete Ribeiro. "GPU Implementation of the Multiple Back-Propagation Algorithm." In Intelligent Data Engineering and Automated Learning - IDEAL 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04394-9_55.

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Yoo, Jang-Hee, Jae-Woo Kim, and Jong-Uk Choi. "An Adaptive Training Method of Back-Propagation Algorithm." In Intelligent Systems Third Golden West International Conference. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-7108-3_55.

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Satish Kumar, K., V. V. S. Sasank, K. S. Raghu Praveen, and Y. Krishna Rao. "Multilayer Perceptron Back propagation Algorithm for Predicting Breast Cancer." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5400-1_5.

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Chen, D. S., and R. C. Jain. "A robust back-propagation learning algorithm for function approximation." In Artificial Intelligence Frontiers in Statistics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4537-2_17.

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Paugam-Moisy, Hélène. "Optimal speedup conditions for a parallel back-propagation algorithm." In Parallel Processing: CONPAR 92—VAPP V. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55895-0_474.

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Mao, Min, and Daowu Pei. "Fuzzy Adaptive Back Propagation Model Based on Genetic Algorithm." In Recent Advances in Computer Science and Information Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25781-0_97.

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Nawi, Nazri Mohd, Muhammad Zubair Rehman, and Abdullah Khan. "A New Bat Based Back-Propagation (BAT-BP) Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01857-7_38.

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Ren, Gang, Qingsong Hua, Pan Deng, and Chao Yang. "FP-MRBP: Fine-grained Parallel MapReduce Back Propagation Algorithm." In Artificial Neural Networks and Machine Learning – ICANN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_77.

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Latifi, Nasim, and Ali Amiri. "Partial and Random Updating Weights in Error Back Propagation Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27337-7_39.

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Nawi, Nazri Mohd, R. S. Ransing, Mohd Najib Mohd Salleh, Rozaida Ghazali, and Norhamreeza Abdul Hamid. "An Improved Back Propagation Neural Network Algorithm on Classification Problems." In Database Theory and Application, Bio-Science and Bio-Technology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17622-7_18.

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Streszczenia konferencji na temat "BACK PROPAGATION ALGORITHM (BPA)"

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Xi, Wu-Dong, Ling Huang, Chang-Dong Wang, Yin-Yu Zheng, and Jianhuang Lai. "BPAM: Recommendation Based on BP Neural Network with Attention Mechanism." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/542.

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Inspired by the significant success of deep learning, some attempts have been made to introduce deep neural networks (DNNs) in recommendation systems to learn users' preferences for items. Since DNNs are well suitable for representation learning, they enable recommendation systems to generate more accurate prediction. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily lead to over-fitting. To tackle these problems, we propose a novel recommendation algorithm based on Back

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Bin, Sun, Zhang Jin, and Zhang Shaoji. "An Investigation of Artificial Neural Network (ANN) in Quantitative Fault Diagnosis for Turbofan Engine." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0032.

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This paper is aimed at investigating two kinds of Artificial Neural Network (ANN) applied to quantitative fault diagnosis of turbofan engine gas path components. Among them, one is Back Propagation neural Network (BPN) and the other is Adaptive Probabilistic Neural Network (APNN). Using BPN in order to achieve quantitative fault diagnosis, number of training samples will increase greatly which may lead to the difficulty of iteration convergence. A new learning rule named hybrid rule is introduced to avoid the algorithm falling into static areas and expedite convergence. Recently, a new method

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Kothari, R., P. Klinkhachorn, and R. S. Nutter. "An accelerated back propagation training algorithm." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170398.

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Roy, Soumava Kumar, and Crefeda Faviola Rodrigues. "Echo Canceller Using Error Back Propagation Algorithm." In 2014 International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2014. http://dx.doi.org/10.1109/iscmi.2014.33.

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Makram-Ebeid, Sirat, and Viala. "A rationalized error back-propagation learning algorithm." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118725.

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Maalej, Z., V. Sleiffer, E. Timmers, et al. "Reduced complexity for back-propagation method algorithm." In 2011 IEEE Photonics Conference (IPC). IEEE, 2011. http://dx.doi.org/10.1109/pho.2011.6110738.

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Nakagawa, Masashi, Takashi Inoue, and Yoshifumi Nishio. "CNN template design using back propagation algorithm." In 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010). IEEE, 2010. http://dx.doi.org/10.1109/cnna.2010.5430327.

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Mishra, B. K., S. K. Singh, and S. Bhala. "Breast cancer diagnosis using back-propagation algorithm." In the International Conference & Workshop. ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980123.

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Varkonyi-Koczy, Annamaria R., and Balazs Tusor. "Improved back-propagation algorithm for neural network training." In 2011 IEEE 7th International Symposium on Intelligent Signal Processing - (WISP 2011). IEEE, 2011. http://dx.doi.org/10.1109/wisp.2011.6051720.

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Fukumi and Omatu. "A new back-propagation algorithm with coupled neuron." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118442.

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Raporty organizacyjne na temat "BACK PROPAGATION ALGORITHM (BPA)"

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Deller, Jr, Hunt J. R., and S. D. A Simple 'Linearized' Learning Algorithm Which Outperforms Back-Propagation. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada249697.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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