In this paper I derive a backpropagation algorithm which is suitable for networks using this type of neuron. The decision surface obtained from this type of network is composed out of elementary hyperoctahedra centered on each point in decision space. Simulations of a simple two-layer feedforward...
A backpropagation algorithm, or backward propagation of errors, is an algorithm that's used to help train neural network models. The algorithm adjusts the network's weights to minimize any gaps -- referred to as errors -- between predicted outputs and the actual target output....
The optimization algorithm repeats a two phase cycle, propagation and weight update. When an input vector is presented to the network, it is propagated forward through the network, layer by layer, until it reaches the output layer. The output of the network is then compared to the desired out...
Backpropagation algorithm is the cornerstone for neural network analysis. Paper extends it for training any derivatives of neural network's output with respect to its input. By the dint of it feedforward networks can be used to solve or verify solutions of partial or simple, linear or nonlinear...
This paper proposes a variation of the standard backpropagation (BP) algorithm that is particularly suitable for training neural networks utilized in multiresolution image classification. The aim is to solve the problem of determining which image resolutions to utilize as network inputs. The approach ...
Gradient and Jacobian calculations were discussed based on backpropagation-through-time (BPTT) algorithm and real-time recurrent learning (RTRL). Some errors in the paper of De Jesus et al. bring difficulties for other researchers who want to implement the algorithms. This comments paper shows the...
In addition, in the conventional BP algorithm, the learning rate is fixed and that it is uniform for all the weights in a layer. In this paper, we propose an efficient acceleration technique, the backpropagation with adaptive learning rate and momentum term, which is based on the convent...
The back-propagation algorithm can be thought of as a way of performing a supervised learning process by means of examples, using the following general approach: A problem, for example, a set of inputs, is presented to the network, and the response from the network is recorded. ...
In this paper, we apply the backpropagation algorithm(Rumelhart et al 1986) to a real-world problem in recognizing handwritten digits taken from the U.S. Mail. Unlike previous results reported by our group on the problem (Denker et al 1989), the learning network is directly fed with images...
A new algorithm is proposed for speeding up the convergence of backpropagation (BP) networks. This algorithm is obtained by applying the momentum term and ... S Sureerattanan,HN Phien - IEEE Apccas the IEEE Asia-pacific Conference on Circuits & Systems 被引量: 8发表: 1998年 Dynamic learning...