There are many different types of neural networks. The operation of the single unit is almost universal and the units are usually but not always, arranged in distinct layers with weighted connections between the layers. Using a neural network to obtain reasonably good results is not difficult. ...
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 ...
In this method, a linear model is constructed around the instance to be explained, and the coefficients are interpreted as the significance of the features. However, this approach is seen as an indirect means of generating explanations as it only focusses on the individual contribution of a ...
justified by humans2. This ‘explainability’ phenomenon limits the usage of ML models in critical real-world applications (e.g., law or traffic management) since the context of a decision is hard to be justified and explained to the end-users. Our proposed social network analysis-based visual...
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)ⱿⱿⱿφlⱿ=σⱿ=11+e−Ɀl=1,2...
The features of MO used jointly with the direct computational graph’s structure in the neural network allowed formulating the fuel design problem to solve it using a standard optimization technique. Here, full-scope and greedy search methods were proposed to identify suitable mixtures in the chemica...
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...
This paper presents an overview on applications of artificial neural network in electric power industry (EPI) which is currently undergoing an extraordinary development. One of the most thrilling and potentially cost-effective recent developments in this field is increasing usage of artificial intelligence...
All depicted sub-ANNs represent fully connected feed-forward ANNs associated with their illustrated in- and outputs and are explained in detail in Appendix A. CANNs accept as an input strain data in form of the Cauchy-Green tensor C as well as (optionally) also non-kinematic data in the ...
Mentalistic capabilities, as we have explained in the chess example, play an important role in reflecting about one’s complex decisions. Again, BDI-like agents can be given both the ability to communicate their decisions to other agents as well as the ability to model the minds of other age...