… neural networks have also been recently extended to model more complex sequential data,
where the temporal dimension can be efficiently processed by exploiting distributed repre…

… theory of distributed intelligence, based on the process of challenge propagation, which
extends the mechanism of spreading activation in neural networks to the collective intelligence …

… Given these requirements, the designed neural network … combination of RNN and convolutional
neural network. However, … We train our neural network to estimate the authenticity of the …

… long short-term memory networks. In CLONE, the learning tasks are solved by a group of
distributed Edge nodes. Each Edge node trains the neural network model locally based on its …

… design of distributed intelligent systems is presented. Finally, the issue of intelligence control
is … investigated application of neural networks is identification and modeling of nonlinear …

Distributed Artificial Intelligence (DAI) is a dynamic area of research and this book is the first
comprehensive, truly integrated exposition of the discipline presenting influential …

… deployment of neural network-based systems, we propose a new technique to train deep
neural networks over several data sources. Our method allows for deep neural networks to be …

… intelligent individual model and the proposed DAI architecture in more detail. Section 15.2
illustrates the concept of an intelligent … the team model of distributed intelligence. Lead by the …

… We investigate Neural Networks (NNs) and … our distributed intelligence. Simulation results
imply that the Digital Ecosystem performs better with the application of a distributed intelligence…

… Methods to estimate linear back box models and models of artificial intelligence, as fuzzy
logic and neural network are presented. The artificial intelligence tools may be used for …