Development And Applications Of A Sequential Minimal Radial Basis Function Rbf Neural Network Learning Algorithm
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Development And Applications Of A Sequential Minimal Radial Basis Function Rbf Neural Network Learning Algorithm
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Author : Ying Wei Lu
language : en
Publisher:
Release Date : 1997
Development And Applications Of A Sequential Minimal Radial Basis Function Rbf Neural Network Learning Algorithm written by Ying Wei Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with categories.
Radial Basis Function Networks 1
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Author : Robert J.Howlett
language : en
Publisher: Physica
Release Date : 2001-03-27
Radial Basis Function Networks 1 written by Robert J.Howlett and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-03-27 with Computers categories.
The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 1 covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms, for example RBF learning using genetic algorithms. Both volumes will prove extremely useful to practitioners in the field, engineers, researchers and technically accomplished managers.
Radial Basis Function Networks 2
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Author : Robert J. Howlett
language : en
Publisher: Physica
Release Date : 2013-03-19
Radial Basis Function Networks 2 written by Robert J. Howlett and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-19 with Computers categories.
The Radial Basis Function (RBF) network has gained in popularity in recent years. This is due to its desirable properties in classification and functional approximation applications, accompanied by training that is more rapid than that of many other neural-network techniques. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of applications areas, for example, robotics, biomedical engineering, and the financial sector. The two-title series Theory and Applications of Radial Basis Function Networks provides a comprehensive survey of recent RBF network research. This volume, New Advances in Design, contains a wide range of applications in the laboratory and case-studies describing current use. The sister volume to this one, Recent Developments in Theory and Applications, covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms. The combination of the two volumes will prove extremely useful to practitioners in the field, engineers, researchers, students and technically accomplished managers.
Radial Basis Neural Network Optimization Using Fruit Fly
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Author : Anurag Rana
language : en
Publisher: GRIN Verlag
Release Date : 2014-06-25
Radial Basis Neural Network Optimization Using Fruit Fly written by Anurag Rana and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-25 with Computers categories.
Master's Thesis from the year 2014 in the subject Computer Sciences - Artificial Intelligence, grade: A, , course: Master Of Technology Computer Science and Engineering, language: English, abstract: This research presents the optimization of radial basis function (RBF) neural network by means of aFOA and establishment of network model, adopting it with the combination of the evaluation of the mean impact value (MIV) to select variables. The form of amended fruit fly optimization algorithm (aFOA) is easy to learn and has the characteristics of quick convergence and not readily dropping into local optimum. The validity of model is tested by two actual examples, furthermore, it is simpler to learn, more stable and practical. Our aim is to find a variable function based on such a large number of experimental data in many scientific experiments such as Near Infrared Spectral data and Atlas data. But this kind of function is often highly uncertain, nonlinear dynamic model. When we perform on the data regression analysis, this requires choosing appropriate independent variables to establish the independent variables on the dependent variables regression model. Generally, experiments often get more variables, some variables affecting the results may be smaller or no influence at all, even some variable acquisition need to pay a large cost. If drawing unimportant variables into model, we can reduce the precision of the model, but cannot reach the ideal result. At the same time, a large number of variables may also exist in multicollinearity. Therefore, the independent variable screening before modeling is very necessary. Because the fruit fly optimization algorithm has concise form, is easy to learn, and have fault tolerant ability, besides algorithm realizes time shorter, and the iterative optimization is difficult to fall into the local extreme value. And radiate basis function (RBF) neural network’s structure is simple, training concise and fasting speed of convergence by learning, can approximate any nonlinear function, having a "local perception field" reputation. For this reason, this paper puts forward a method of making use of the amended fruit flies optimization algorithm to optimize RBF neural network (aFOA-RBF algorithm) using for variable selection.
Radial Basis Function Networks 2
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Author : Robert J. Howlett
language : en
Publisher: Physica
Release Date : 2001-03-27
Radial Basis Function Networks 2 written by Robert J. Howlett and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-03-27 with Computers categories.
The Radial Basis Function (RBF) network has gained in popularity in recent years. This is due to its desirable properties in classification and functional approximation applications, accompanied by training that is more rapid than that of many other neural-network techniques. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of applications areas, for example, robotics, biomedical engineering, and the financial sector. The two-title series Theory and Applications of Radial Basis Function Networks provides a comprehensive survey of recent RBF network research. This volume, New Advances in Design, contains a wide range of applications in the laboratory and case-studies describing current use. The sister volume to this one, Recent Developments in Theory and Applications, covers advances in training algorithms, variations on the architecture and function of the basis neurons, and hybrid paradigms. The combination of the two volumes will prove extremely useful to practitioners in the field, engineers, researchers, students and technically accomplished managers.
Radial Basis Function Networks 2
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Author : Robert J. Howlett
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-03-27
Radial Basis Function Networks 2 written by Robert J. Howlett and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-03-27 with Computers categories.
The Radial Basis Function (RBF) neural network has gained in popularity over recent years because of its rapid training and its desirable properties in classification and functional approximation applications. RBF network research has focused on enhanced training algorithms and variations on the basic architecture to improve the performance of the network. In addition, the RBF network is proving to be a valuable tool in a diverse range of application areas, for example, robotics, biomedical engineering, and the financial sector. The two volumes provide a comprehensive survey of the latest developments in this area. Volume 2 contains a wide range of applications in the laboratory and case studies describing current industrial use. Both volumes will prove extremely useful to practitioners in the field, engineers, reserachers, students and technically accomplished managers.
Second Order Training Algorithms For Radial Basis Function Neural Network
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Author : Kanishka Tyagi
language : en
Publisher:
Release Date : 2011
Second Order Training Algorithms For Radial Basis Function Neural Network written by Kanishka Tyagi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
A systematic two step batch approach for constructing and training of Radial basis function (RBF) neural networks is presented. Unlike other RBF learning algorithms, the proposed paradigm uses optimal learning factors (OLF's) to train the network parameters, i.e. spread parameters, mean vector parameters and weighted distance measure (DM) coefficients. Newton's algorithm is proposed for obtaining multiple optimal learning factors (MOLF) for the network parameters. The weights connected to the output layer are trained by a supervisedlearning algorithm based on orthogonal least squares (OLS). The error obtained is then backpropagated to tune the RBF parameters. The proposed hybrid training algorithm has been compared with the Levenberg Marquardt and recursive least square based RLS-RBF training algorithms. Simulation results show that regardless of the input data dimension, the proposed algorithms are a significant improvement in terms of convergence speed, network size and generalization over conventional RBF training algorithms which use a single optimal learning factor (SOLF). Analyses of the proposed training algorithms on noisy input data have also been carried out. The ability of the proposed algorithm is further substantiated by using k-fold cross validation. Initialization of network parameters using Self Organizing Map (SOM), efficient calculation of Hessian matrix for network parameters, Newton's method for optimization, optimal learning factors and orthogonal least squares are the subject matter of present work.
Regularized Radial Basis Function Networks
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Author : Paul V. Yee
language : en
Publisher: Wiley-Interscience
Release Date : 2001-04-16
Regularized Radial Basis Function Networks written by Paul V. Yee and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-04-16 with Technology & Engineering categories.
Simon Haykin is a well-known author of books on neural networks. * An authoritative book dealing with cutting edge technology. * This book has no competition.
Research Anthology On Artificial Neural Network Applications
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2021-07-16
Research Anthology On Artificial Neural Network Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-16 with Computers categories.
Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.
Extreme Learning Machines 2013 Algorithms And Applications
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Author : Fuchen Sun
language : en
Publisher: Springer
Release Date : 2014-07-08
Extreme Learning Machines 2013 Algorithms And Applications written by Fuchen Sun and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-08 with Technology & Engineering categories.
In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability. This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discussions of “learning without iterative tuning". This book covers algorithms and applications of ELM. It gives readers a glance of the newest developments of ELM.