Relevant bibliographies by topics / Resilient back propagation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Resilient back propagation'

Author: Grafiati
Published: 3 June 2025
Last updated: 13 July 2025

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Journal articles on the topic "Resilient back propagation"

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Garkani-Nejad, Zahra, and Behzad Ahmadi-Roudi. "Investigating the role of weight update functions in developing artificial neural network modeling of retention times of furan and phenol derivatives." Canadian Journal of Chemistry 91, no. 4 (2013): 255–62. http://dx.doi.org/10.1139/cjc-2012-0372.

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A quantitative structure−retention relationship study has been carried out on the retention times of 63 furan and phenol derivatives using artificial neural networks (ANNs). First, a large number of descriptors were calculated using HyperChem, Mopac, and Dragon softwares. Then, a suitable number of these descriptors were selected using a multiple linear regression technique. This paper focuses on investigating the role of weight update functions in developing ANNs. Therefore, selected descriptors were used as inputs for ANNs with six different weight update functions including the Levenberg−Marquardt back-propagation network, scaled conjugate gradient back-propagation network, conjugate gradient back-propagation with Powell−Beale restarts network, one-step secant back-propagation network, resilient back-propagation network, and gradient descent with momentum back-propagation network. Comparison of the results indicates that the Levenberg−Marquardt back-propagation network has better predictive power than the other methods.
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Yang, Fei, Pengdong Gao, and Yongquan Lu. "Evolving Resilient Back-Propagation Algorithm for Energy Efficiency Problem." MATEC Web of Conferences 77 (2016): 06016. http://dx.doi.org/10.1051/matecconf/20167706016.

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Chithambaram, T., and K. Perumal. "Comparative Study: Artificial Neural Networks Training Functions for Brain Tumor Segmentation for MRI Images." Journal of Computational and Theoretical Nanoscience 17, no. 4 (2020): 1831–38. http://dx.doi.org/10.1166/jctn.2020.8448.

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Brain tumor detection from medical images is essential to diagnose earlier and to take decision in treatment planning. Magnetic Resonance Images (MRI) is frequently preferred for detecting brain tumors by the physicians. This paper analyses various Artificial Neural Networks (ANN) training functions for brain tumor segmentation such as Levenberg-Marquardt (LM), Quasi Newton back propagation (QN), Bayesian regularization (BR), Resilient back propagation algorithm (RP) and Scaled conjugate gradient back propagation (SCG). The training algorithms were employed in different sized network for segmentation. The results were carefully analyzed and measured using Dice similarity, sensitivity, specificity and accuracy measures.
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Mastorocostas, P. A. "Resilient back propagation learning algorithm for recurrent fuzzy neural networks." Electronics Letters 40, no. 1 (2004): 57. http://dx.doi.org/10.1049/el:20040052.

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Ervina, Mertha Endah, Rini Silvi, and Intaniah Ratna Nur Wisisono. "Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network." Jurnal Matematika "MANTIK" 4, no. 2 (2018): 90–99. http://dx.doi.org/10.15642/mantik.2018.4.2.90-99.

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Train scheduling affects the level of customer satisfaction and profitability of the train service provider. The prediction method of Back-propagation Neural Network (BPNN) has relatively slow convergence. Therefore, this study uses Resilient Back-propagation (Rprop) because it has a more fast convergence and high accuracy. The model produced is a model for Jabodetabek, Java (non-Jabodetabek), Sumatra, and Indonesia. From the results of data analysis conducted, it can be concluded that the performance of neural network model with Resilient Back-propagation (Rprop) formed from training data gives very accurate prediction accuracy level with mean absolute percentage error (MAPE) less than 10% for each model. Then forecasting for the next 12 months conducted and the results compared with the data testing, Rprop provides a very high forecasting accuracy with MAPE value below 10%. The MAPE value for each forecasting the number of rail passengers is 7.50% for Jabodetabek, 5.89% for Java (non-Jabodetabek), 5.36% for Sumatra and 4.80% for Indonesia. That is, four neural network architectures with Rprop can be used for this case with very accurate forecasting results.
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Almiani, Muder, Alia Abughazleh, Yaser Jararweh, and Abdul Razaque. "Resilient Back Propagation Neural Network Security Model For Containerized Cloud Computing." Simulation Modelling Practice and Theory 118 (July 2022): 102544. http://dx.doi.org/10.1016/j.simpat.2022.102544.

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SubramanyaNayak, G., and Dayananda Nayak. "Classification of ECG Signals using ANN with Resilient Back Propagation Algorithm." International Journal of Computer Applications 54, no. 6 (2012): 20–23. http://dx.doi.org/10.5120/8570-2294.

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P., Anil Kumar, and Anuradha B. "Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Algorithms." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2795–803. https://doi.org/10.11591/ijece.v8i5.pp2795-2803.

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Pattern recognition has been acknowledged as one of the promising research areas and it has drawn the awareness among many researchers since its existence at the beginning of the nineties. Multilayer Neural networks are used in pattern Recognition and classification based on the features derived from the input patterns. The Reflectivity information extracted from the Doppler Weather Radar (DWR) image helps in identifying the convective cloud type which has a strong relation to the precipitation rate. The reflectivity information is rooted in the DWR image with the help of colors and color bar is provided to distinguish among different reflectivity information. Artificial Neural network predicts the color based on the maximum likelihood estimation problem. This paper presents a best possible backpropagation algorithm for color identification in DWR images by comparing various backpropagation algorithms such as Levenberg-Marquardt, Conjugate gradient, and Resilient back propagation etc.,. Pattern recognition using Neural networks presents better results compared to standard distance measures. It is observed that Levenberg-Marquardt backpropagation algorithm yields a regression value of 99% approximately and accuracy of 98%.
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Ayyıldız, Mustafa, and Kerim Çetinkaya. "Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 6 (2016): 1206–16. http://dx.doi.org/10.1177/0954408916659310.

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In this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, five different learning algorithms were used: the Levenberg–Marquardt, the quasi-Newton back propagation, the scaled conjugate gradient, the resilient back propagation, and the conjugate gradient back propagation. The best result was obtained by 6-5-1 network architectures with single hidden layers for the geometric shapes. After artificial neural network training, the correlation coefficients ( R2) of the geometric shape values for training and testing data were very close to 1. Similarly, the root-mean-square error and mean error percentage values for the training and testing data were less than 0.9% and 0.004%, respectively. These results demonstrated that the artificial neural network is an admissible model for the estimation of geometric shapes using image processing.
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Jyotshna, Dongardive, and Abraham Siby. "BRAIN Journal - Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 6, no. 1-2 (2015): 22–29. https://doi.org/10.5281/zenodo.1044169.

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ABSTRACT The paper proposes a neural network based approach to predict secondary structure of protein. It uses Multilayer Feed Forward Network (MLFN) with resilient back propagation as the learning algorithm. Point Accepted Mutation (PAM) is adopted as the encoding scheme and CB396 data set is used for the training and testing of the network. Overall accuracy of the network has been experimentally calculated with different window sizes for the sliding window scheme and by varying the number of units in the hidden layer. The best results were obtained with eleven as the window size and seven as the number of units in the hidden layer.
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Dissertations / Theses on the topic "Resilient back propagation"

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Melo, Geisla de Albuquerque. "UTILIZAÇÃO DE PROCESSAMENTO DIGITAL DE IMAGENS E REDES NEURAIS ARTIFICIAIS PARA O RECONHECIMENTO DE ÍNDICES DE SEVERIDADE DA FERRUGEM ASIÁTICA DA SOJA." UNIVERSIDADE ESTADUAL DE PONTA GROSSA, 2015. http://tede2.uepg.br/jspui/handle/prefix/129.

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Made available in DSpace on 2017-07-21T14:19:24Z (GMT). No. of bitstreams: 1 Melo, Geisla Albuquerque.pdf: 2986772 bytes, checksum: 02494f1ef68a9df48a1184c0a3e81dce (MD5) Previous issue date: 2015-05-25<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>According to Embrapa (2013), Brazil is the world's second largest soy producer just after the United States. Season after season, the production and planted area in Brazil is growing, however, climatic factors and crop diseases are affecting plantation, preventing further growth, and causing losses to farmers. Asian rust caused by Phakopsora pachyrhizi, is a foliar disease, considered one of the most important diseases at present, because of the potential for loss. Asian rust can be mistaken for other diseases in soybeans, such as Bacterial Blight, a Stain Brown and Bacterial Pustule, due to similar visual appearances. Thus, the present study aimed to develop an application for mobile devices using the Android platform to perform automatic recognition of the Asian soybean rust severity indices to assist in the early diagnosis and therefore assist in decision-making as the management and control of the disease. For this, was used techniques of digital image processing (DIP) and Artificial Neural Networks (ANN). First, around 3.000 soybean leaves were collected in the field, where about 2.000 were harnessed. Then it were separated by severity index, photographed in a controlled environment, and after that were processed in order to eliminate noise and background images. Filtering preprocessing phase consisted of median filter, Gaussian filter processing for gray scale, Canny edge detector, expansion, find and drawcontours, and finally the cut of leaf. After this was extracted color and texture features of the images, which were the average R, G and B Variant also for the three channels R, G and B according angular momentum, entropy, contrast, homogeneity, and finally correlation the severity degree previously known. With these data, the training was performed an ANN through the neural network simulator BrNeural. During training, parameters such as number of severity levels and number of neurons of the hidden layer have changed. After training, was chosen network architecture that gave better results, with 78.86% accuracy for Resilient-propagation algorithm. This network was saved in an object and inserted into the application, ready to be used with new data. Thus, the application takes the soybean leaf picture and filters the acquired image. After this, it extracts the features and commands internally to the trained neural network, which analyzes and reports the severity. Still, it is optionally possible to see a georeferenced map of the property, with the severities identified by small colored squares, each representing a different index.<br>Segundo a Embrapa (2013), o Brasil é o segundo maior produtor de soja do mundo, atrás apenas nos Estados Unidos. Safra após safra, a produção e a área plantada do Brasil vem crescendo, entretanto, fatores climáticos e doenças da cultura vêm afetando as lavouras, impedindo um crescimento ainda maior, e causando perdas para os agricultores. A ferrugem asiática, causada pelo fungo Phakopsora pachyrhizi, é uma doença foliar, considerada uma das doenças de maior importância na atualidade, devido ao grande potencial de perdas. A ferrugem asiática pode ser confundida com outras doenças na soja, como o Crestamento Bacteriano, a Mancha Parda e a Pústula Bacteriana, devido às aparências visuais semelhantes. Deste modo, O presente estudo teve por objetivo desenvolver um aplicativo para dispositivos móveis que utilizam a plataforma Android, para realizar o reconhecimento automático dos índices de severidade da ferrugem asiática da soja, para auxiliar no diagnóstico precoce e por consequência, auxiliar na tomada de decisão quanto ao manejo e controle da doença. Para isto, foram utilizadas técnicas de Processamento Digital de Imagens (PDI) e Redes Neurais Artificiais (RNA). Primeiramente, foram coletadas aproximadamente 3 mil folhas de soja em campo, onde cerca de 2 mil foram aproveitadas. Então elas foram separadas por índices de severidade, fotografadas em ambiente controlado, e após isto foram processadas com o objetivo de eliminar ruídos e o fundo das imagens. A fase de filtragem do pré-processamento consistiu nos filtros da mediana, filtro Gaussiano, transformação para escala de cinza, detector de bordas Canny, dilatação, find e drawcontours, e por fim o recorte da folha. Após isto, foram extraídas as características de cor e textura das imagens, que foram as médias R, G e B, Variância também para os três canais R, G e B, Segundo Momento Angular, Entropia, Contraste, Homogeneidade, Correlação e por fim, o Grau de Severidade previamente sabido. Com estes dados, foi realizado o treinamento de uma RNA através do simulador de redes neurais BrNeural. Durante o treinamento, parâmetros como quantidade de níveis de severidade e quantidade de neurônios da camada oculta foram alterados. Após o treinamento, foi escolhida a arquitetura de rede que deu melhor resultado, com 78,86% de acerto para o algoritmo Resilient-propagation. Esta rede foi salva em um objeto e inserida no aplicativo, pronta para ser utilizada com dados novos. Assim, o aplicativo tira a foto da folha de soja e faz a filtragem da imagem adquirida. Após isto, extrai as características e manda internamente para a rede neural treinada, que analisa e informa a severidade. Ainda, opcionalmente é possível ver um mapa georreferenciado da propriedade, com as severidades identificadas por pequenos quadrados coloridos, representando cada um, um índice diferente.
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TUAN, MU HO-YI, and 端木和奕. "Taiwan 4thgeneration stocks with Multiple resilient back-propagation neural models." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/78922808896083689933.

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碩士<br>中華大學<br>資訊管理學系碩士班<br>103<br>Stock market investment and financial management tools .Was Taiwan plople long-term use, and the share price reflect the value of the company in the market. 4th Generation technology in recent years by the impact of stock to produce 4G stocks, the stock vulnerable to introduction of new products fluctuate, How to find tomorrow's ups and downs rules from historical data, The use of artificial intelligence to carry out an objective point of view of data mining elect and future stock price related indicators, and to establish a predictive model to provide decision-making. In this study, Are three 4G stocks, for example, collected in 2012 to 2014, dozens of technology information and indicators, Use Classification And Regression Tree screening the most relevant indicators of closing price, Then use Fuzzy Clustering Method to grouping test data into Back-propagation neural network, of the last time this model and to compare the results of econometric models. Classification And Regression Tree，CART filter out the results D (9), ADX (14) have screened out of the three models, it deserves to be Probe, Back-propagation neural network to predict for UMC Accuracy attain 89.86%,and the output value near to actual value, This study presents the model representative of the trend for change has good predictive ability, three stocks accuracy than ARIMA group higher of 20%, Representing multiple resilient back-propagation neural models, At stock market forecast with a given reference value.
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Chen, Chien-Hsiang, and 陳建翔. "Application of Stock Technical Indicators on Resilient Back-Propagation Forecasting Stock Price-Taking Semiconductor Industry as Example." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/dh3ku7.

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碩士<br>輔仁大學<br>金融與國際企業學系金融碩士班<br>106<br>The purpose of this research is to apply stock technical indicators to the effectiveness of machine learning in predicting stock prices. First of all, this study uses feature variable transformation and variable screening to improve the speed of model learning, and uses a step-by-step test method to find the best hidden layer, and set training stop conditions. Finally, this study chooses Resilient Back-propagation method to build stock price forecasting model, and summarizes the model assessment method established in this study. Finally, the model will use test data to apply to stock technical indicators, and evaluate the effectiveness of the combination of technical indicators strategy and model. The study finds that in this study, under the application of the R-language neuralnet suite, the model predicts stock prices can be effectively forecasted on mid-to-long-term trends, and the forecast of stock prices can be applied to technical indicators and converted into prediction signals. It can provide investors with transaction operation reference, and based on different technical indicator strategies, different price forecasting models can be constructed to improve its prediction accuracy and obtain more effective investment reference signals.
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Fick, Machteld. "Neurale netwerke as moontlike woordafkappingstegniek vir Afrikaans." Diss., 2002. http://hdl.handle.net/10500/584.

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Text in Afrikaans<br>Summaries in Afrikaans and English<br>In Afrikaans, soos in NederJands en Duits, word saamgestelde woorde aanmekaar geskryf. Nuwe woorde word dus voortdurend geskep deur woorde aanmekaar te haak Dit bemoeilik die proses van woordafkapping tydens teksprosessering, wat deesdae deur rekenaars gedoen word, aangesien die verwysingsbron gedurig verander. Daar bestaan verskeie afkappingsalgoritmes en tegnieke, maar die resultate is onbevredigend. Afrikaanse woorde met korrekte lettergreepverdeling is net die elektroniese weergawe van die handwoordeboek van die Afrikaanse Taal (HAT) onttrek. 'n Neutrale netwerk ( vorentoevoer-terugpropagering) is met sowat. 5 000 van hierdie woorde afgerig. Die neurale netwerk is verfyn deur 'n gcskikte afrigtingsalgoritme en oorfragfunksie vir die probleem asook die optimale aantal verborge lae en aantal neurone in elke laag te bepaal. Die neurale netwerk is met 5 000 nuwe woorde getoets en dit het 97,56% van moontlike posisies korrek as of geldige of ongeldige afkappingsposisies geklassifiseer. Verder is 510 woorde uit tydskrifartikels met die neurale netwerk getoets en 98,75% van moontlike posisies is korrek geklassifiseer.<br>In Afrikaans, like in Dutch and German, compound words are written as one word. New words are therefore created by simply joining words. Word hyphenation during typesetting by computer is a problem, because the source of reference changes all the time. Several algorithms and techniques for hyphenation exist, but results are not satisfactory. Afrikaans words with correct syllabification were extracted from the electronic version of the Handwoordeboek van die Afrikaans Taal (HAT). A neural network (feedforward backpropagation) was trained with about 5 000 of these words. The neural network was refined by heuristically finding a suitable training algorithm and transfer function for the problem as well as determining the optimal number of layers and number of neurons in each layer. The neural network was tested with 5 000 words not the training data. It classified 97,56% of possible points in these words correctly as either valid or invalid hyphenation points. Furthermore, 510 words from articles in a magazine were tested with the neural network and 98,75% of possible positions were classified correctly.<br>Computing<br>M.Sc. (Operasionele Navorsing)
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Book chapters on the topic "Resilient back propagation"

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Alankar, Bhavya, Nowsheena Yousf, and Shafqat Ul Ahsaan. "Predictive Analytics for Weather Forecasting Using Back Propagation and Resilient Back Propagation Neural Networks." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9330-3_10.

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Temurtas, Fevzullah, Nejat Yumusak, Rustu Gunturkun, Hasan Temurtas, and Osman Cerezci. "Elman’s Recurrent Neural Networks Using Resilient Back Propagation for Harmonic Detection." In PRICAI 2004: Trends in Artificial Intelligence. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28633-2_45.

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Das, Raja, and Mohan Kumar Pradhan. "Artificial Neural Network Training Algorithms in Modeling of Radial Overcut in EDM." In Soft Computing Techniques and Applications in Mechanical Engineering. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3035-0.ch006.

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This chapter describes with the comparison of the most used back propagations training algorithms neural networks, mainly Levenberg-Marquardt, conjugate gradient and Resilient back propagation are discussed. In the present study, using radial overcut prediction as illustrations, comparisons are made based on the effectiveness and efficiency of three training algorithms on the networks. Electrical Discharge Machining (EDM), the most traditional non-traditional manufacturing procedures, is growing attraction, due to its not requiring cutting tools and permits machining of hard, brittle, thin and complex geometry. Hence it is very popular in the field of modern manufacturing industries such as aerospace, surgical components, nuclear industries. But, these industries surface finish has the almost importance. Based on the study and test results, although the Levenberg-Marquardt has been found to be faster and having improved performance than other algorithms in training, the Resilient back propagation algorithm has the best accuracy in testing period.
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Das, Raja, and Mohan Kumar Pradhan. "Artificial Neural Network Training Algorithms in Modeling of Radial Overcut in EDM." In Research Anthology on Artificial Neural Network Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch015.

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This chapter describes with the comparison of the most used back propagations training algorithms neural networks, mainly Levenberg-Marquardt, conjugate gradient and Resilient back propagation are discussed. In the present study, using radial overcut prediction as illustrations, comparisons are made based on the effectiveness and efficiency of three training algorithms on the networks. Electrical Discharge Machining (EDM), the most traditional non-traditional manufacturing procedures, is growing attraction, due to its not requiring cutting tools and permits machining of hard, brittle, thin and complex geometry. Hence it is very popular in the field of modern manufacturing industries such as aerospace, surgical components, nuclear industries. But, these industries surface finish has the almost importance. Based on the study and test results, although the Levenberg-Marquardt has been found to be faster and having improved performance than other algorithms in training, the Resilient back propagation algorithm has the best accuracy in testing period.
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Amitab, Khwairakpam, Debdatta Kandar, and Arnab K. Maji. "Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image." In Research Advances in the Integration of Big Data and Smart Computing. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8737-0.ch016.

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Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.
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Amitab, Khwairakpam, Debdatta Kandar, and Arnab K. Maji. "Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image." In Deep Learning and Neural Networks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch028.

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Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.
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M., Fawzi, and Ali H. "Resilient Back Propagation Algorithm for Breast Biopsy Classification Based on Artificial Neural Networks." In Computational Intelligence and Modern Heuristics. InTech, 2010. http://dx.doi.org/10.5772/7817.

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Benes, Peter Mark, Miroslav Erben, Martin Vesely, Ondrej Liska, and Ivo Bukovsky. "HONU and Supervised Learning Algorithms in Adaptive Feedback Control." In Advances in Computational Intelligence and Robotics. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0063-6.ch002.

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This chapter is a summarizing study of Higher Order Neural Units featuring the most common learning algorithms for identification and adaptive control of most typical representatives of plants of single-input single-output (SISO) nature in the control engineering field. In particular, the linear neural unit (LNU, i.e., 1st order HONU), quadratic neural unit (QNU, i.e. 2nd order HONU), and cubic neural unit (CNU, i.e. 3rd order HONU) will be shown as adaptive feedback controllers of typical models of linear plants in control including identification and control of plants with input time delays. The investigated and compared learning algorithms for HONU will be the step-by-step Gradient Descent adaptation with the study of known modifications of learning rate for improved convergence, the batch Levenberg-Marquardt algorithm, and the Resilient Back-Propagation algorithm. The theoretical achievements will be summarized and discussed as regards their usability and the real issues of control engineering tasks.
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Conference papers on the topic "Resilient back propagation"

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Prasad, Navneel, Rajeshni Singh, and Sunil Pranit Lal. "Comparison of Back Propagation and Resilient Propagation Algorithm for Spam Classification." In 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation (CIMSim). IEEE, 2013. http://dx.doi.org/10.1109/cimsim.2013.14.

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Halim, Shamimi A., Azlin Ahmad, Norzaidah Md Noh, Mohd Shazuan B. Md Ali Safudin, and Rashidi Ahmad. "A comparative study between standard Back Propagation and Resilient Propagation on snake identification accuracy." In 2011 International Symposium on Information Technology in Medicine and Education (ITME 2011). IEEE, 2011. http://dx.doi.org/10.1109/itime.2011.6132031.

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Wang, Rui, and Yi Jiang. "An Interference-Resilient Relay Beamforming Scheme Inspired by Back-Propagation Algorithm." In 2020 Information Theory and Applications Workshop (ITA). IEEE, 2020. http://dx.doi.org/10.1109/ita50056.2020.9245001.

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Chie, Szu-Lin Su, He-Nian Shou, and Wen-Hsiung Liu. "Resilient back-propagation neural network for approximation weighted geometric dilution of precision." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5563546.

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Qi, Zhang, Xie Xiufen, Liu Guofu, and Liu Bo. "Attenuating the Wheel Speed Sensor Errors Based on Resilient Back Propagation Neural Network." In 2007 8th International Conference on Electronic Measurement and Instruments. IEEE, 2007. http://dx.doi.org/10.1109/icemi.2007.4351247.

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Asawa, K., N. Wadhwa, and S. Agrahari. "Resilient Back Propagation based Yield Prediction of Keratinase from Bacillus Megaterium SN1." In IASTED Technology Conferences 2010. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.728-009.

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Reddy, L. Rajesh, Preet Patel, and Shah Krupa Rajendra. "Utilization of Resilient Back Propagation Algorithm and Discrete Wavelet Transform for the Differential Protection of Three Phase Power Transformer." In 2020 21st National Power Systems Conference (NPSC). IEEE, 2020. http://dx.doi.org/10.1109/npsc49263.2020.9331861.

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Liu, Tong, Yuelei Wang, and Peng Cai. "The Calculation of the Tube Wall Temperature of Superheater in Boiler Based on the Resilient Back Propagation Artificial Neural Network." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.294.

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Scott, Daniel J., and David C. Jensen. "Implementation of Network Optimization and Resiliency Analysis Towards Mission Assurance." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-142272.

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Abstract:
Abstract Network optimization and resiliency analysis are pivotal domains revealing network functionality, strength, and resilience. Despite their promise, these methodologies often encounter integration limitations, scalability issues, or functional gaps, prompting the need for further development. This paper explores the characteristics of network optimization and resiliency analysis, presenting existing methodologies, discussing their limitations, and proposing a new approach. Our proposed method integrates genetic algorithms (GAs) and weighted degradation analysis to address these shortcomings effectively. Our research emphasizes the integration of GAs for network optimization and weighted degradation analysis for resiliency assessment. Through a case study focusing on an electrical grid system, we elucidate the efficacy and challenges of our proposed methodologies. While GAs efficiently generate optimized networks, they encounter inconsistencies due to algorithmic issues. Similarly, weighted degradation analysis demonstrates forward propagation capabilities but faces challenges in assessing back-propagation issues adequately. Network optimization and resiliency analysis play a crucial role in understanding network characteristics, contributing to overall network assurance and mission success. This study underscores the importance of ongoing refinement and resolution of algorithmic issues to fully leverage these methodologies. Further research is essential to overcoming existing challenges and seamlessly integrating these methodologies into mission-critical systems, thereby enhancing network understanding and optimization capabilities.
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