Various complex activation functions are considered and a practical definition is proposed. The method, associated to a mean-square-error criterion, yields the complex form of the conventional backpropagation algorithm关键词: Theoretical or Mathematical/ neural nets signal processing/ coefficients updating ...
The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that
The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that
7.1.1 Differentiable activation functions The backpropagation algorithm looks for the minimum of the error function in weight space using the method of gradient descent. The combination of weights which minimizes the error function is considered to be a solution of the learning problem. Since this ...
In order to overcome the existence of the local minimum in the multilayer perceptron (MLP) implemented with back-propagation (BP) algorithm, the evolutionary strategy (ES) is proposed. Introducing the factors of the chromosome and gene mutation rates, one can enhance the flexibility of the mutatio...
During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses are embedded within multilayered networks, making it difficult to determine the effect of an individual synaptic modification on the behaviour of the system. The backpropagation algorithm solves this problem in ...
A highly efficient implementation of back propagation algorithm using matrix instruction set architecture Summary: Back Propagation (BP) training algorithm has received intensive research efforts to exploit its parallelism in order to reduce the training time for complex problems. A modified version of BP...
Recall from Chapter 2 that when running the backpropagation algorithm we need to compute the network's output error, δLδL. The form of the output error depends on the choice of cost function: different cost function, different form for the output error. For the cross-entropy the output ...
This is typically performed via the classic back-propagation algorithm (Rumelhart et al. 1988). For further details, see Goodfellow et al. (2016). Fig. 1 Feed-forward fully connected neural network Full size image 3.2.2 Auto-encoder An auto-encoder NN is an unsupervised model used to learn...
predict disease based on the input variables that it is presented. This is accomplished through a process known as back-propagation of error, which utilizes a gradient descent algorithm (a form of hill climbing) that seeks to minimize the error of the values that are output from the neural ...