Bibliografías temáticas / Neural network RBF

Literatura académica sobre el tema "Neural network RBF"

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Publicado: 4 de mayo de 2024

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Artículos de revistas sobre el tema "Neural network RBF"

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Zhu, Jian Min, Peng Du y Ting Ting Fu. "Research for RBF Neural Networks Modeling Accuracy of Determining the Basis Function Center Based on Clustering Methods". Advanced Materials Research 317-319 (agosto de 2011): 1529–36. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1529.

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The radial basis function (RBF) neural network is superior to other neural network on the aspects of approximation ability, classification ability, learning speed and global optimization etc., it has been widely applied as feedforward networks, its performance critically rely on the choice of RBF centers of network hidden layer node. K-means clustering, as a commonly method used on determining RBF center, has low neural network generalization ability, due to its clustering results are not sensitive to initial conditions and ignoring the influence of dependent variable. In view of this problem, fuzzy clustering and grey relational clustering methods are proposed to substitute K-means clustering, RBF center is determined by the results of fuzzy clustering or grey relational clustering, and some researches of RBF neural networks modeling accuracy are done. Practical modeling cases demonstrate that the modeling accuracy of fuzzy clustering RBF neural networks and grey relational clustering RBF neural networks are significantly better than K-means clustering RBF neural networks, applying of fuzzy clustering or grey relational clustering to determine the basis function center of RBF neural networks hidden layer node is feasible and effective.
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Luan, Tiantian, Mingxiao Sun, Guoqing Xia y Daidai Chen. "Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate". Complexity 2018 (22 de octubre de 2018): 1–19. http://dx.doi.org/10.1155/2018/6950124.

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The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi-information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS-RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self-organizing RBF neural networks (self-organizing RBF (SORBF) and rough RBF neural networks (RS-RBF)), VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS-RBF.
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Yakovyna, V. S. "Software failures prediction using RBF neural network". Odes’kyi Politechnichnyi Universytet. Pratsi, n.º 2 (15 de junio de 2015): 111–18. http://dx.doi.org/10.15276/opu.2.46.2015.20.

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Wen, Hui, Tao Yan, Zhiqiang Liu y Deli Chen. "Integrated neural network model with pre-RBF kernels". Science Progress 104, n.º 3 (julio de 2021): 003685042110261. http://dx.doi.org/10.1177/00368504211026111.

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To improve the network performance of radial basis function (RBF) and back-propagation (BP) networks on complex nonlinear problems, an integrated neural network model with pre-RBF kernels is proposed. The proposed method is based on the framework of a single optimized BP network and an RBF network. By integrating and connecting the RBF kernel mapping layer and BP neural network, the local features of a sample set can be effectively extracted to improve separability; subsequently, the connected BP network can be used to perform learning and classification in the kernel space. Experiments on an artificial dataset and three benchmark datasets show that the proposed model combines the advantages of RBF and BP networks, as well as improves the performances of the two networks. Finally, the effectiveness of the proposed method is verified.
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Liu, Yunbing. "Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management". Journal of Control Science and Engineering 2022 (30 de junio de 2022): 1–6. http://dx.doi.org/10.1155/2022/7025223.

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Aiming at the nonlinear time series of automatic building construction management, a neural network prediction model is proposed to analyze and process the nonlinear sequence of deformation monitoring number cutter. The specific content of this method is as follows: for the noise problems existing in deformation monitoring data, a wavelet is used to denoise the preprocessing; for the BP network and RBF network commonly used in neural networks, the performance of the two networks is compared and demonstrated by MATLAB program, which proves that RBF neural network can significantly improve the accuracy of deformation prediction. By comparing the results, the maximum relative error of BP network prediction is 18.59%, while the maximum relative error of RBF network prediction is 29.16%, and the average relative error of 13P network prediction is 7.02%, while the average relative error of RBF network prediction value is 10.5%. The comprehensive error of network prediction is 6.1%, RBF network prediction is 8.52%, the standard deviation RMSE of BP network prediction error is 15.347, and that of RBF network prediction error is 21.401, and it shows that the prediction accuracy of BP network is higher than that of RBF network.
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Yu, Fa Hong, Mei Jia Chen y Wei Zhi Liao. "A Novel Learning Evaluation Method Based on RBF Neural Network". Applied Mechanics and Materials 385-386 (agosto de 2013): 1697–700. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1697.

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There are many learning evaluation methods, but most of them are subjective, which contains a lot of man-made factors. This paper presents a new learning evaluation method based on radial basis function (RBF) neutral network. By analysis the orthogonal least squares for RBF and determines the center of the basis functions, the model of RBF neural network was constructed. Experimental studies show that the Method Based on RBF Neural Network is effective for learning Evaluation.
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Li, Hui Jun y Li Zhang. "Prediction of Tensile Strength Based on RBF Neural Network". Advanced Materials Research 476-478 (febrero de 2012): 1309–12. http://dx.doi.org/10.4028/www.scientific.net/amr.476-478.1309.

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The objective of this research is to predict yarn tensile strength. The model of predicting yarn tensile strength is built based on RBF neural network. The RBF neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of yarn. The results show prediction models based on RBF neural network are very precise and efficient.
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Liu, Dong Dong. "A Method about Load Distribution of Rolling Mills Based on RBF Neural Network". Advanced Materials Research 279 (julio de 2011): 418–22. http://dx.doi.org/10.4028/www.scientific.net/amr.279.418.

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Rolling mills process is too complicated to be described by formulas. RBF neural networks can establish finishing thickness and rolling force models. Traditional models are still useful to the neural network output. Compared with those finishing models which have or do not have traditional models as input, the importance of traditional models in application of neural networks is obvious. For improving the predictive precision, BP and RBF neural networks are established, and the result indicates that the model of load distribution based on RBF neural network is more accurate.
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Tsoulos, Ioannis G., Alexandros Tzallas y Evangelos Karvounis. "A Two-Phase Evolutionary Method to Train RBF Networks". Applied Sciences 12, n.º 5 (25 de febrero de 2022): 2439. http://dx.doi.org/10.3390/app12052439.

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This article proposes a two-phase hybrid method to train RBF neural networks for classification and regression problems. During the first phase, a range for the critical parameters of the RBF network is estimated and in the second phase a genetic algorithm is incorporated to locate the best RBF neural network for the underlying problem. The method is compared against other training methods of RBF neural networks on a wide series of classification and regression problems from the relevant literature and the results are reported.
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Yu, Ying. "GDP Economic Forecasting Model Based on Improved RBF Neural Network". Mathematical Problems in Engineering 2022 (9 de septiembre de 2022): 1–11. http://dx.doi.org/10.1155/2022/7630268.

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Among the existing GDP forecasting methods, time series forecasting and regression model forecasting are the two most commonly used forecasting methods. However, traditional macroeconomic forecasting models are unable to accurately achieve optimal forecasts of highly complex nonlinear dynamic macroeconomic systems due to the influence of multiple confounding factors. In order to solve the above problems, a GDP economic forecasting model based on an improved RBF neural network is proposed. First, the main traditional GDP forecasting methods are analyzed. Then, RBF neural networks are used to solve the problem that traditional forecasting technology methods cannot handle multi-factor complex nonlinearities well. Second, to further improve the convergence speed and accuracy of the RBF neural network learning algorithm, the Shuffled Frog Leaping Algorithm with global search capability and high practicality is fused into the RBF network training. Finally, the improved RBF neural network is used to build a GDP economic forecasting model. The performance of the Shuffled Frog Leaping Algorithm and the improved RBF neural network was tested using the approximation of Hermit polynomials and the Iris classification problem as simulation examples. The experimental results show that the improved RBF neural network-based GDP economic forecasting model achieves more accurate forecasting accuracy than other forecasting methods.
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Tesis sobre el tema "Neural network RBF"

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FERREIRA, Aida Araújo. "Comparação de arquiteturas de redes neurais para sistemas de reconheceimento de padrões em narizes artificiais". Universidade Federal de Pernambuco, 2004. https://repositorio.ufpe.br/handle/123456789/2465.

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Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco
Um nariz artificial é um sistema modular composto de duas partes principais: um sistema sensor, formado de elementos que detectam odores e um sistema de reconhecimento de padrões que classifica os odores detectados. Redes neurais artificiais têm sido utilizadas como sistema de reconhecimento de padrões para narizes artificiais e vêm apresentando resultados promissores. Desde os anos 80, pesquisas para criação de narizes artificiais, que permitam detectar e classificar odores, vapores e gases automaticamente, têm tido avanços significativos. Esses equipamentos podem ser utilizados no monitoramento ambiental para controlar a qualidade do ar, na área de saúde para realizar diagnóstico de doenças e nas indústrias de alimentos para o controle de qualidade e o monitoramento de processos de produção. Esta dissertação investiga a utilização de quatro técnicas diferentes de redes neurais para criação de sistemas de reconhecimento de padrões em narizes artificiais. O trabalho está dividido em quatro partes principais: (1) introdução aos narizes artificiais, (2) redes neurais artificiais para sistema de reconhecimento de padrões, (3) métodos para medir o desempenho de sistemas de reconhecimento de padrões e comparar os resultados e (4) estudo de caso. Os dados utilizados para o estudo de caso, foram obtidos por um protótipo de nariz artificial composto por um arranjo de oito sensores de polímeros condutores, expostos a nove tipos diferentes de aguarrás. Foram adotadas as técnicas Multi-Layer Perceptron (MLP), Radial Base Function (RBF), Probabilistic Neural Network (PNN) e Time Delay Neural Network (TDNN) para criar os sistemas de reconhecimento de padrões. A técnica PNN foi investigada em detalhes, por dois motivos principais: esta técnica é indicada para realização de tarefas de classificação e seu treinamento é feito em apenas um passo, o que torna a etapa de criação dessas redes muito rápida. Os resultados foram comparados através dos valores dos erros médios de classificação utilizando o método estatístico de Teste de Hipóteses. As redes PNN correspondem a uma nova abordagem para criação de sistemas de reconhecimento de padrões de odor. Estas redes tiveram um erro médio de classificação de 1.1574% no conjunto de teste. Este foi o menor erro obtido entre todos os sistemas criados, entretanto mesmo com o menor erro médio de classificação, os testes de hipóteses mostraram que os classificadores criados com PNN não eram melhores do que os classificadores criados com a arquitetura RBF, que obtiveram um erro médio de classificação de 1.3889%. A grande vantagem de criar classificadores com a arquitetura PNN foi o pequeno tempo de treinamento dos mesmos, chegando a ser quase imediato. Porém a quantidade de nodos na camada escondida foi muito grande, o que pode ser um problema, caso o sistema criado deva ser utilizado em equipamentos com poucos recursos computacionais. Outra vantagem de criar classificadores com redes PNN é relativa à quantidade reduzida de parâmetros que devem ser analisados, neste caso apenas o parâmetro relativo à largura da função Gaussiana precisou ser investigado
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Damasceno, Nielsen Castelo. "Separa??o cega de fontes lineares e n?o lineares usando algoritmo gen?tico, redes neurais artificiais RBF e negentropia de R?nyi como medida de independ?ncia". Universidade Federal do Rio Grande do Norte, 2010. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15358.

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Conventional methods to solve the problem of blind source separation nonlinear, in general, using series of restrictions to obtain the solution, often leading to an imperfect separation of the original sources and high computational cost. In this paper, we propose an alternative measure of independence based on information theory and uses the tools of artificial intelligence to solve problems of blind source separation linear and nonlinear later. In the linear model applies genetic algorithms and R?nyi of negentropy as a measure of independence to find a separation matrix from linear mixtures of signals using linear form of waves, audio and images. A comparison with two types of algorithms for Independent Component Analysis widespread in the literature. Subsequently, we use the same measure of independence, as the cost function in the genetic algorithm to recover source signals were mixed by nonlinear functions from an artificial neural network of radial base type. Genetic algorithms are powerful tools for global search, and therefore well suited for use in problems of blind source separation. Tests and analysis are through computer simulations
Os m?todos convencionais para resolver o problema de separa??o cega de fontes n?o lineares em geral utilizam uma s?rie de restri??es ? obten??o da solu??o, levando muitas vezes a uma n?o perfeita separa??o das fontes originais e alto custo computacional. Neste trabalho, prop?e-se uma alternativa de medida de independ?ncia com base na teoria da informa??o e utilizam-se ferramentas da intelig?ncia artificial para resolver problemas de separa??o cega de fontes lineares e posteriormente n?o lineares. No modelo linear aplica-se algoritmos gen?ticos e a Negentropia de R?nyi como medida de independ?ncia para encontrar uma matriz de separa??o linear a partir de misturas lineares usando sinais de forma de ondas, ?udios e imagens. Faz-se uma compara??o com dois tipos de algoritmos de An?lise de Componentes Independentes bastante difundidos na literatura. Posteriormente, utiliza-se a mesma medida de independ?ncia como fun??o custo no algoritmo gen?tico para recuperar sinais de fontes que foram misturadas por fun??es n?o lineares a partir de uma rede neural artificial do tipo base radial. Algoritmos gen?ticos s?o poderosas ferramentas de pesquisa global e, portanto, bem adaptados para utiliza??o em problemas de separa??o cega de fontes. Os testes e as an?lises se d?o atrav?s de simula??es computacionais
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Pham, Hoang Anh. "Coordination de systèmes sous-marins autonomes basée sur une méthodologie intégrée dans un environnement Open-source". Electronic Thesis or Diss., Toulon, 2021. http://www.theses.fr/2021TOUL0020.

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Cette thèse étudie la coordination de robots sous-marins autonomes dans le contexte d’exploration de fonds marins côtiers ou d’inspections d’installations. En recherche d’une méthodologie intégrée, nous avons créé un framework qui permet de concevoir et simuler des commandes de robots sous-marins low-cost avec différentes hypothèses de modèle de complexité croissante (linéaire, non-linéaire, et enfin non-linéaire avec des incertitudes). Sur la base de ce framework articulant plusieurs outils, nous avons étudié des algorithmes pour résoudre le problème de la mise en formation d’un essaim, puis celui de l’évitement de collisions entre robots et celui du contournement d’obstacle d’un groupe de robots sous-marins. Plus précisément, nous considérons d'abord les modèles de robot sous-marin comme des systèmes linéaires de type simple intégrateur, à partir duquel nous pouvons construire un contrôleur de mise en formation en utilisant des algorithmes de consensus et d’évitement. Nous élargissons ensuite ces algorithmes pour le modèle dynamique non linéaire d’un robot Bluerov dans un processus de conception itératif. Nous intégrons ensuite un réseau de neurones de type RBF (Radial Basis Function), déjà éprouvé en convergence et stabilité, avec le contrôleur algébrique pour pouvoir estimer et compenser des incertitudes du modèle du robot. Enfin, nous décrivons les tests de ces algorithmes sur un essaim de robots sous-marins réels BlueROV en environement Opensource de type ROS et programmés en mode autonome. Ce travail permet également de convertir un ROV téléopéré en un hybride ROV-AUV autonome. Nous présentons des résultats de simulation et des essais réels en bassin validant les concepts proposés
This thesis studies the coordination of autonomous underwater robots in the context of coastal seabed exploration or facility inspections. Investigating an integrated methodology, we have created a framework to design and simulate low-cost underwater robot controls with different model assumptions of increasing complexity (linear, non-linear, and finally non-linear with uncertainties). By using this framework, we have studied algorithms to solve the problem of formation control, collision avoidance between robots and obstacle avoidance of a group of underwater robots. More precisely, we first consider underwater robot models as linear systems of simple integrator type, from which we can build a formation controller using consensus and avoidance algorithms. We then extend these algorithms for the nonlinear dynamic model of a Bluerov robot in an iterative design process. Then we have integrated a Radial Basis Function neural network, already proven in convergence and stability, with the algebraic controller to estimate and compensate for uncertainties in the robot model. Finally, we have presented simulation results and real basin tests to validate the proposed concepts. This work also aims to convert a remotely operated ROV into an autonomous ROV-AUV hybrid
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Soukup, Jiří. "Metody a algoritmy pro rozpoznávání obličejů". Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2008. http://www.nusl.cz/ntk/nusl-374588.

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This work is describing basic methods of face recognition. The methods PCA, LDA, ICA, trace tranfsorm, elastic bunch graph map, genetic algorithm and neural network are described. In practical part, the PCA, PCA + RBF neural network and genetic algorithms are implemented. The RBF neural network is used in the way of clasificator and genetic algorithm is used for RBF NN training in one case and for selecting eigenvectors from PCA method in the other case. This method, PCA + GA, called EPCA, outperform other methods tested in this work on the ORL testing database.
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Li, Junxu. "A Dynamic Parameter Tuning Algorithm For Rbf Neural Networks". Fogler Library, University of Maine, 1999. http://www.library.umaine.edu/theses/pdf/LiJ1999.pdf.

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Guo, Zhihao. "Intelligent multiple objective proactive routing in MANET with predictions on delay, energy, and link lifetime". online version, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1195705509.

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Medagam, Peda Vasanta Reddy. "Online optimal control for a class of nonlinear system using RBF neural networks /". Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1650508351&sid=19&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Machado, Madson Cruz. "Sintonia RNA-RBF para o Projeto Online de Sistemas de Controle Adaptativo". Universidade Federal do Maranhão, 2017. http://tedebc.ufma.br:8080/jspui/handle/tede/1744.

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The need to increase industrial productivity coupled with quality and low cost requirements has generated a demand for the development of high performance controllers. Motivated by this demand, we presented in this work models, algorithms and a methodology for the online project of high-performance control systems. The models have characteristics of adaptability through adaptive control system architectures. The models developed were based on artificial neural networks of radial basis function type, for the online project of model reference adaptive control systems associated with the of sliding modes control. The algorithms and the embedded system developed for the online project were evaluated for tracking mobile targets, in this case, the solar radiation. The control system has the objective of keeping the surface of the photovoltaic module perpendicular to the solar radiation, in this way the energy generated by the module will be as high as possible. The process consists of a photovoltaic panel coupled in a structure that rotates around an axis parallel to the earth’s surface, positioning the panel in order to capture the highest solar radiation as function of its displacement throughout the day.
A necessidade de aumentar a produtividade industrial, associada com os requisitos de qualidade e baixo custo, gerou uma demanda para o desenvolvimento de controladores de alto desempenho. Motivado por esta demanda, apresentou-se neste trabalho modelos, algoritmos e uma metodologia para o projeto online de sistemas de controle de alto desempenho. Os modelos apresentam características de adaptabilidade por meio de arquiteturas de sistemas de controle adaptativo. O desenvolvimento de modelos, baseia-se em redes neurais artificiais (RNA), do tipo função de base radial (RBF, radial basis function), para o projeto online de sistemas de controle adaptativo do tipo modelo de referência associado com o controle de modos deslizantes (SMC, sliding mode control). Os algoritmos e o sistema embarcado desenvolvidos para o projeto online são avaliados para o rastreamento de alvos móveis, neste caso, o rastreamento da radiação solar. O sistema de controle tem o objetivo de manter a superfície do módulo fotovoltaico perpendicular à radiação solar, pois dessa forma a energia gerada pelo módulo será a maior possível. O processo consiste de um painel fotovoltaico acoplado em uma estrutura que gira em torno de um eixo paralelo à superfície da terra, posicionando o painel de forma a capturar a maior radiação solar em função de seu deslocamento ao longo do dia.
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Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics". Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.
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Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics". University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.
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Libros sobre el tema "Neural network RBF"

1

Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34816-7.

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Hong, X. A Givens rotation based fast backward elimination algorithm for RBF neural network pruning. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Radial Basis Function Rbf Neural Network Control For Mechanical Systems Design Analysis And Matlab Simulation. Springer-Verlag Berlin and Heidelberg GmbH &, 2013.

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Luppi, Pierre-Hervé, Olivier Clément, Christelle Peyron y Patrice Fort. Neurobiology of REM sleep. Editado por Sudhansu Chokroverty, Luigi Ferini-Strambi y Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0003.

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REM (paradoxical) sleep is a state characterized by rapid eye movements, EEG activation, and muscle atonia. REM sleep behavior disorder (RBD) is a parasomnia characterized by loss of muscle atonia during REM sleep. Cataplexy, a key symptom of narcolepsy, is a striking sudden episode of muscle weakness comparable to REM sleep atonia triggered by emotions during wakefulness. This chapter presents recent results on the neuronal network responsible for REM sleep and explores hypotheses explaining RBD and cataplexy. RBD could be due to a specific degeneration of glutamatergic neurons responsible for muscle atonia, localized in the pontine sublaterodorsal tegmental nucleus (SLD) or the glycinergic/GABAergic premotoneurons localized in the ventral medullary reticular nuclei. Cataplexy in narcoleptics could be due to activation during waking of SLD neurons. In normal conditions, activation of SLD neurons would be blocked by simultaneous excitation by hypocretins of REM sleep-off GABAergic neurons localized in the ventrolateral periaqueductal gray.
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Göknar, Izzet y Levent Sevgi. Complex Computing-Networks: Brain-Like and Wave-oriented Electrodynamic Algorithms. Springer London, Limited, 2006.

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(Editor), I. C. Göknar y L. Sevgi (Editor), eds. Complex Computing-Networks : Brain-like and Wave-oriented Electrodynamic Algorithms (Springer Proceedings in Physics) (Springer Proceedings in Physics). Springer, 2006.

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Capítulos de libros sobre el tema "Neural network RBF"

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Burdsall, B. y C. Giraud-Carrier. "GA-RBF: A Self-Optimising RBF Network". En Artificial Neural Nets and Genetic Algorithms, 346–49. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_76.

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Liu, Jinkun. "Adaptive RBF Neural Network Control". En Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 71–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_4.

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Liu, Jinkun. "Digital RBF Neural Network Control". En Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 293–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_9.

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Liu, Jinkun. "Discrete RBF Neural Network Control". En Intelligent Control Design and MATLAB Simulation, 215–34. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5263-7_10.

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Liu, Jinkun. "Adaptive RBF Neural Network Control". En Intelligent Control Design and MATLAB Simulation, 159–87. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5263-7_8.

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Liu, Jinkun. "RBF Neural Network Design and Simulation". En Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, 19–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34816-7_2.

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Li, JingBing, HuaiQiang Zhang, YouLing Zhou y Yong Bai. "RBF Neural Network Case Teaching Research". En Communications in Computer and Information Science, 351–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22418-8_49.

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Wei, Xiaotao, Houkuan Huang y Shengfeng Tian. "A Modified RBF Neural Network for Network Anomaly Detection". En Advances in Neural Networks - ISNN 2006, 261–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760191_38.

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Roberts, Stephen y Lionel Tarassenko. "Automated Sleep EEg Analysis using an RBF Network". En Applications of Neural Networks, 305–20. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2379-3_13.

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Gueorguieva, Natacha y Iren Valova. "Building RBF Neural Network Topology through Potential Functions". En Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 1033–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_123.

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Actas de conferencias sobre el tema "Neural network RBF"

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Servin, M. y F. J. Cuevas. "New kind of classifier neural network using RBFs". En OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mii3.

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Neural networks based on RBFs have recently gained a wider acceptance over the backpropagation topology because of its faster learning rates due to the decoupling between the hidden and output layers.1 We have derived still another network topology based on normalized RBFs, which do not require learning time. This normalization process makes the classifying network behave radically different from the conventional RBFs classifier, because it creates sharp classifying boundaries among the classes. This new topology has been applied to the classical problem of character recognition degraded by a noisy binary channel. The classifying properties obtained by this system are equivalent to a system formed by a classical RBF approximation followed by a winner-takes-all network (WTA), without the disadvantages of the convergence time required by a WTA network. Also, as mentioned above, a powerful advantage of this network is that is does not require learning time. Only a representative number of templates are needed; they can be fed into the network without any prior processing.
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Titsias, M. y A. Likas. "A probabilistic RBF network for classification". En Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. http://dx.doi.org/10.1109/ijcnn.2000.860779.

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Bi, Jing, Kun Zhang y Xiaojing Cheng. "Intrusion Detection Based on RBF Neural Network". En 2009 International Symposium on Information Engineering and Electronic Commerce (IEEC). IEEE, 2009. http://dx.doi.org/10.1109/ieec.2009.80.

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Zhou, Kaili, Yaohong Kang, Yan Huang y Erli Feng. "Encrypting Algorithm Based on RBF Neural Network". En Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.353.

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Li, Zhang, Xu Qingyang, Jin Shibo y Li Jiangning. "Coking flue temperature RBF neural network model". En 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7161862.

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Shijie, Yan y Wang Xu. "RBF Neural Network Adaptive Control of Microturbine". En 2009 WRI Global Congress on Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/gcis.2009.66.

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Xiang-Bin Yan, Zhen Wang, Shu-Hua Yu y Yi-Jun Li. "Time series forecasting with RBF neural network". En Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527764.

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Wang, Lei y Zhongyi Zuo. "Travel Mode Recognition Using RBF Neural Network". En 14th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413623.069.

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Kanojia, Mahendra G. y Siby Abraham. "Breast cancer detection using RBF neural network". En 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2016. http://dx.doi.org/10.1109/ic3i.2016.7917990.

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Arthy, G. y C. N. Marimuthu. "Immune RBF neural network algorithm for DSTATCOM". En 2016 International Conference on Computer Communication and Informatics. IEEE, 2016. http://dx.doi.org/10.1109/iccci.2016.7480035.

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Informes sobre el tema "Neural network RBF"

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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang y Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), julio de 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
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Alwan, Iktimal, Dennis D. Spencer y Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, diciembre de 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p<0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p<0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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