Improved error signal of the backpropagation (BP) algorithm on single processors has shown a tremendous result compared to its counterpart [1]. Further study on the improved BP algorithm is carried out on many processors, which is implemented using the Sequent Symmetry SE30 machine. Data partitioning method, with columnwise block striped and batch mode weight updating strategy, is applied on the BP algorithms. Twenty-six patterns consisting of uppercase letters from ‘A’ to ‘Z’ are tested in terms of speed and recognition rates. The parallel version of the BP algorithm produces good speedup as the numbers of processors are increased and a 100% recognition rate for trained and untrained data is achieved
The Back-Propagation (BP) Neural Network (NN) is probably the most well known of all neural networks...
This paper presents an efficient mapping scheme for the multilayer perceptron (MLP) network trained ...
This paper presents an efficient mapping scheme for the multilayer perceptron (MLP) network trained ...
Improved error signal of the backpropagation (BP) algorithm on single processors has shown a tremend...
This paper demonstrates how the backpropagation algorithm (BP) and its variants can be accelerated s...
Artificial neural networks have applications in many fields ranging from medicine to image processin...
Abstract—The well known backpropagation learning algo-rithm is implemented in a FPGA board and a mic...
Some adaptations are proposed to the basic BP algorithm in order to provide in efficient method to n...
The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of imp...
This paper introduces a data preprocessing algorithm that can improve the efficiency of the standard...
One of the major issues in using artificial neural networks is reducing the training and the testing...
Standard Backpropagation Algorithm (BP) is a widely used algorithm in training Neural Network that i...
Abstract — In this paper, we present an efficient technique for mapping a backpropagation (BP) learn...
This paper presents a mapping scheme for parallel pipelined execution of the Backpropagation Learnin...
The Back-Propagation (BP) Neural Network (NN) is probably the most well known of all neural networks...
The Back-Propagation (BP) Neural Network (NN) is probably the most well known of all neural networks...
This paper presents an efficient mapping scheme for the multilayer perceptron (MLP) network trained ...
This paper presents an efficient mapping scheme for the multilayer perceptron (MLP) network trained ...
Improved error signal of the backpropagation (BP) algorithm on single processors has shown a tremend...
This paper demonstrates how the backpropagation algorithm (BP) and its variants can be accelerated s...
Artificial neural networks have applications in many fields ranging from medicine to image processin...
Abstract—The well known backpropagation learning algo-rithm is implemented in a FPGA board and a mic...
Some adaptations are proposed to the basic BP algorithm in order to provide in efficient method to n...
The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of imp...
This paper introduces a data preprocessing algorithm that can improve the efficiency of the standard...
One of the major issues in using artificial neural networks is reducing the training and the testing...
Standard Backpropagation Algorithm (BP) is a widely used algorithm in training Neural Network that i...
Abstract — In this paper, we present an efficient technique for mapping a backpropagation (BP) learn...
This paper presents a mapping scheme for parallel pipelined execution of the Backpropagation Learnin...
The Back-Propagation (BP) Neural Network (NN) is probably the most well known of all neural networks...
The Back-Propagation (BP) Neural Network (NN) is probably the most well known of all neural networks...
This paper presents an efficient mapping scheme for the multilayer perceptron (MLP) network trained ...
This paper presents an efficient mapping scheme for the multilayer perceptron (MLP) network trained ...