controller designusing Muli Layer Perceptron (MLP) structure is described in[108]. The design of a multilayer perceptron neural network (MLPNN) controller for LFC issues in a two area deregulated power system is explained in[109]. A three layerfeed forwardneural network (NN) is proposed for ...
Lin YJ. Explaining critical clearing time with the rules extracted from a multilayer perceptron artificial neural network. International Journal of Electrical Power & Energy Systems. 2010; 32(8): 873-878.Lin YJ. Explaining critical clearing time with the rules extracted from a multilayer perceptron ...
response to natural sounds using a linear mapping fit to the responses to a subset of the sounds with ridge regression. Model predictions were evaluated on held-out sounds.d, Average voxel response variance explained by the best-predicting stage of each auditory model from Figs.2and5plotted agai...
AI tools and services are evolving at a rapid rate. Current innovations can be traced back to the 2012 AlexNet neural network, which ushered in a new era of high-performance AI built on GPUs and large data sets. The key advancement was the discovery that neural networks could be trained on...
The textbook meaning of an artificial neural network (ANN) is adeep learningmodel made up of neurons that emulate the structure of the human brain. These neurons are designed to mimic the way nerve cells in the mind work while enabling the model to process and learn from a dataset independen...
In order to assure th's property in the neural network explained in the previous section, we use a weight sharing mechanism and keep the weights of the twin (opposite) edges in the neural network equal through the learning process, such that wi,j = wj,i. The proposed architecture is ...
The architecture of neurons and electrical activity are introduced from the perspective of biological neural networks, and the neural network mechanism of information transmission and information memory is explained in Chapter 1. The development, characteristics and applications of artificial neural network ...
Which is explained by the fact that the number of nodes in the hidden layers defines the complexity and power of the neural network model to delineate underlaying relationships and structures inherent to the database. The number of nodes in the hidden layers should be large enough for the ...
Both activation functions are duly explained in Appendix B. Furthermore, the output of j − th neuron (j = 1, 2, ⋯, ml) at l − th layer is given by yl, j = φl( vl, j( n)), whereas the φl(∙) is explained as(3)φlⱿ=σⱿ=11+e−Ɀl=1,2,⋯,L...
As explained in Majout et al. (2022c), Majout et al. (2022a), the major drawback of SMC control is embodied in the chattering issue. This problem arises because of the discontinuous sign function used in the construction of SMC, which leads to low control accuracy, high wear of ...