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
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,...
Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting t
Community structure is also known as clustering in complex networks in the field of complex networks. People have been studying community structure for a long time, and algorithms for discovering community structure, such as the greedy algorithm, are becoming more mature. Although neural networks are...
2.1. Backpropagation The backpropagation algorithm involves main phases: the forward and backward passes. In a forward pass, we compute the network outputs by passing the input object to the input layer, through each hidden layer, and to the output layer. This phase uses the current weights an...
DCNN is trained to minimize the loss based on the back propagation algorithm. Adam algorithm is chosen as the optimizer in our experiment. The initial learning rate is set as 0.001, and it will be divided by 10 for every 30 epochs until convergences. Considering the actual situation of the...
The HDC learning is based on the back-propagation algorithm optimized by minimizing the cross-entropy loss. It is composed of four stages: output layer learning, LSTM network learning, MLP network learning, and CNN learning. 4.1. Output layer learning Assume we have a training sample xi with ...
The autoencoder is an unsupervised neural network model employing the backpropagation algorithm. Typically, it consists of two modules: the encoder and the decoder. The encoder maps input data into a lower-dimensional latent space, which is then mapped back to the original data space by the deco...
The backpropagation algorithm is used to optimize the network parameters, resulting in a more accurate prediction model. Therefore, compared to other neural network models, the multilayer perceptron has outstanding advantages in nonlinear modeling, training speed, and handling of input variable correlation...
The optimal scheduling problem of integrated energy system (IES) has the characteristics of high-dimensional nonlinearity. Using the traditional Grey Wolf Optimizer (GWO) to solve the problem, it is easy to fall into a local optimum in the process of opt