1). This leads to the weight transport problem (a), which has been solved by using symmetric learning rules to maintain weight symmetry50,52,53 or with the Kolen-Pollack algorithm53,54,55, which leads to symmetric weights automatically. It has also been found that weights do not have to ...
a separate feedback network for the backpropagation of errors has been proposed49,50,51(see Fig.1). This leads to the weight transport problem (a), which has been solved by using symmetric learning rules to maintain weight symmetry50,52,53or with the Kolen-Pollack algorithm53,...
For example, Gilra and Gerstner50 developed a spiking model in which feedback about the error on the output directly affects the activity of hidden neurons before plasticity takes place. Haider et al.51 developed a faster inference algorithm for energy-based models that computes a value to ...
test of a network model and learning algorithm. The XOR function maps two binary inputs to a single binary output. This simple Boolean func- tion is not linearly separable (i.e. it cannot be solved by a simple mapping directly from the in- puts to the output), and thus requires the ...
The multistage optimal control problem can be solved in various ways, as, e.g., by the application of dynamic programming. Within the backpropagation framework, weights are tuned layer-by-layer as well as step-by-step to minimize the learning error. Meanwhile, in the new algorithm for each...
The problem of extraction of crisp logical rules from neuralnnetworks trained with a backpropagation algorithm is solved by smoothntransformation of these networks into simpler networks performingnlogical functions. Two constraints are included in the cost function: anregularization term inducing weight ...
The Backpropagation Algorithm The backpropagation algorithm is based on generalizing the Widrow-Hoff learning rule. It uses supervised learning, which means that the algorithm is provided with examples of the inputs and outputs that the network should compute, and then the error is calculated. The...
The BP neural network algorithm has the ability of arbitrary complex pattern classification and excellent multidimensional function mapping, which can solve other problems such as XOR, which cannot be solved by simple perceptrons. Structurally, the BP network has an input layer, a hidden layer, and...
Have you solved this problem? jemshit commented Oct 3, 2018 via email No need to do anything manually. Optimizer algorithm (SGD, Adadelta...) will use backpropagation. … eliethesaiyan commented Dec 23, 2018 @jemshit ,i think what @jerryli1981 meant is to be able to apply a ...
Reference14 proposed the genetic algorithm back propagation model (GA-BP). The optimized BP neural network is used to make short-term prediction of BDS clock bias, and the results show that its accuracy is better than that of the BP neural network and GM (1,1) model, which shows the ...