This chapter provides a review of the intimate relationships between neurons and microglia, by introducing 2D and 3D fractal analysis methodology and its applications in neuron鈥搈icroglia function in health and disease.Karperien, Audrey L.
,tjm] is the target label of the j-th training sample. It is noted that n and m denote the number of input and output neurons, respectively. L is the number of hidden neurons and G(x) is the activation function. SLFNs can be expressed as: ∑i=1LβiGi(xj)=∑i=1LβiG(ωixj+...
Fig. 4 shows the structure of the ANN model, and any neuron is connected to all the neurons in the next layer, and the connections between neurons are assigned appropriate weights (Zhou et al., 2020). In an iterative procedure, the training learning algorithm continuously modifies the weights...
The prediction is based on how fast the variation between the predicted and the actual output is calculated to get the prediction error, which is subsequently used to vary the weights of the neurons in all of the previous layers (backpropagation) until it reaches the optimal prediction accuracy...
7. Deep Belief Network DBN is a hybrid generation model composed of restricted Boltzmann machine and Sigmaid belief network. It can identify, classify, and generate data by training the weight between its neurons. 8. Convolutional Neural Network A CNN performs dimension reduction and feature ...
In summary, the deep learning methods based on the 3D-CNN makes full use of the structural characteristics of 3D HSI data. However, although these 3D-CNN models can extract features directly from the original HSI data, when the network becomes deeper, the degradation phenomenon will occur [39...
The ELM classifier was fitted on the same training subset with a single hidden layer of 1000 neurons and a training ratio value of 0.9 to find the combination of nodes, weights, and biases minimizing the error between the actual output of the network (predictions) and the expected one (the...