Theoretical study about neural networks, especially their types of topologies and networks learning. Special attention is attended to multilayer neural network with learning backpropagation. Introduced learning algorithm backpropagation of simple networks in conjunction with descriptions of parameters affecting network learning also methods to exaluation quality of network learning. Definition moment invariants to rotation, translation and scaling. Optimalization parameters of neural networks to find the network which has the fastest learning and also the networks with the best value of recognition patterns of letters from testing set
Bachelor's thesis describes the basics of issue of multilayer neural networks and explains principle...
Abstract:- We present in this article a new approach for multilayer perceptrons ’ training. It is ba...
Supervised Learning in Multi-Layered Neural Networks (MLNs) has been recently proposed through the w...
In studies of neural networks, the Multilavered Feedforward Network is the most widely used network ...
Methods to speed up learning in back propagation and to optimize the network architecture have been ...
Backpropagation is a supervised learning algorithm for training multi-layer neural networks for func...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
In this paper a review of fast-learning algorithms for multilayer neural networks is presented. From...
The work presented in this thesis is mainly involved in the study of Artificial Neural Networks (ANN...
Rumelhart, Hinton and Williams [Rumelhart et al. 86] describe a learning procedure for layered netwo...
Ellerbrock TM. Multilayer neural networks : learnability, network generation, and network simplifica...
Backpropagation (BP) is one of the most widely used algorithms for training feed-forward neural netw...
The back-propagation algorithm calculates the weight changes of an artificial neural network, and a ...
Summary form only given, as follows. A novel learning algorithm for multilayered neural networks is ...
Neural networks as a general mechanism for learning and adaptation became increasingly popular in re...
Bachelor's thesis describes the basics of issue of multilayer neural networks and explains principle...
Abstract:- We present in this article a new approach for multilayer perceptrons ’ training. It is ba...
Supervised Learning in Multi-Layered Neural Networks (MLNs) has been recently proposed through the w...
In studies of neural networks, the Multilavered Feedforward Network is the most widely used network ...
Methods to speed up learning in back propagation and to optimize the network architecture have been ...
Backpropagation is a supervised learning algorithm for training multi-layer neural networks for func...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
In this paper a review of fast-learning algorithms for multilayer neural networks is presented. From...
The work presented in this thesis is mainly involved in the study of Artificial Neural Networks (ANN...
Rumelhart, Hinton and Williams [Rumelhart et al. 86] describe a learning procedure for layered netwo...
Ellerbrock TM. Multilayer neural networks : learnability, network generation, and network simplifica...
Backpropagation (BP) is one of the most widely used algorithms for training feed-forward neural netw...
The back-propagation algorithm calculates the weight changes of an artificial neural network, and a ...
Summary form only given, as follows. A novel learning algorithm for multilayered neural networks is ...
Neural networks as a general mechanism for learning and adaptation became increasingly popular in re...
Bachelor's thesis describes the basics of issue of multilayer neural networks and explains principle...
Abstract:- We present in this article a new approach for multilayer perceptrons ’ training. It is ba...
Supervised Learning in Multi-Layered Neural Networks (MLNs) has been recently proposed through the w...