Second-order and Nonsmooth Training Methods for Fuzzy Neural Networks PDF Download
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Author: Christian Eitzinger
Publisher:
ISBN:
Category : Fuzzy systems
Languages : en
Pages : 164
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Book Description
Author: Christian Eitzinger
Publisher:
ISBN:
Category : Fuzzy systems
Languages : en
Pages : 164
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Book Description
Author: Peitsang Wu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Oliver Nelles
Publisher: Springer Science & Business Media
ISBN: 3662043238
Category : Technology & Engineering
Languages : en
Pages : 785
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Book Description
Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.
Author: Yong-Chul Shin
Publisher:
ISBN:
Category :
Languages : en
Pages : 284
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Book Description
Author: Parastoo Kheirkhah
Publisher:
ISBN:
Category :
Languages : en
Pages : 82
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Book Description
A systematic two-step batch approach for constructing a sparsely connected neural network is presented. Unlike other sparse neural networks, the proposed paradigm uses orthogonal least squares (OLS) to train the network. OLS based pruning is proposed to induce sparsity in the network. Based on the usefulness of the basic functions in the hidden units, the weights connecting the output to hidden units and output to input units are modified to form a sparsely connected neural network. The proposed hybrid training algorithm has been compared with the fully connected MLP and sparse softmax classifier that uses second order training algorithm. The simulation results show that the proposed algorithm has significant improvement in terms of convergence speed, network size, generalization and ease of training over fully connected MLP. Analysis of the proposed training algorithm on various linear and non-linear data files is carried out. The ability of the proposed algorithm is further substantiated by clearly differentiating two separate datasets when feed into the proposed algorithm. The experimental results are reported using 10-fold cross validation. Inducing sparsity into a fully connected neural network, pruning of the hidden units, Newton's method for optimization, and orthogonal least squares are the subject matter of the present work.
Author: Yi Ren
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
We also show that, under rather mild conditions, the algorithm converges to a stationary point if Levenberg-Marquardt damping is used. The results of a substantial number of numerical experiments are reported in Chapters 2, 3, 4 and 5, in which we compare the performance of our methods to state-of-the-art methods used to train DNNs, that demonstrate the efficiency and effectiveness of our proposed new second-order methods.
Author: Somnath Bhattacharyya
Publisher: Springer Nature
ISBN: 9811536155
Category : Mathematics
Languages : en
Pages : 497
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Book Description
This book features original research papers presented at the International Conference on Computational and Applied Mathematics, held at the Indian Institute of Technology Kharagpur, India during November 23–25, 2018. This book covers various topics under applied mathematics, ranging from modeling of fluid flow, numerical techniques to physical problems, electrokinetic transport phenomenon, graph theory and optimization, stochastic modelling and machine learning. It introduces the mathematical modeling of complicated scientific problems, discusses micro- and nanoscale transport phenomena, recent development in sophisticated numerical algorithms with applications, and gives an in-depth analysis of complicated real-world problems. With contributions from internationally acclaimed academic researchers and experienced practitioners and covering interdisciplinary applications, this book is a valuable resource for researchers and students in fields of mathematics, statistics, engineering, and health care.
Author: Thomas George
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
First order optimization methods (gradient descent) have enabled impressive successes for training artificial neural networks. Second order methods theoretically allow accelerating optimization of functions, but in the case of neural networks the number of variables is far too big. In this master's thesis, I present usual second order methods, as well as approximate methods that allow applying them to deep neural networks. I introduce a new algorithm based on an approximation of second order methods, and I experimentally show that it is of practical interest. I also introduce a modification of the backpropagation algorithm, used to efficiently compute the gradients required in optimization.
Author: Ke-Lin Du
Publisher: Springer Science & Business Media
ISBN: 1447155718
Category : Technology & Engineering
Languages : en
Pages : 834
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Book Description
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.
Author: Kanishka Tyagi
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
