Multi Layer Adaptive Networks
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MULTI-LAYER ADAPTIVE NETWORKS.
The report is concerned with logic networks whose logical properties are adaptable in response to error signals relating to the networks' performance. That is, a device is envisioned which receives inputs, possesses certain variable internal parameters, and produces outputs which depend on both the inputs and the internal parameters. If these outputs are not those desired, error indications to the device should cause such a change in the devices's internal parameters as will properly correct these outputs. If such a device is composed of a network of the variable-weight threshold elements described below, their weights being the required internal parameters, then the device will be called a learning network. Learning networks are of considerable importance because of the possibilities they offer for producing networks whose logical properties as given by their weight values are too complex to have been devised by hand. That is, it may transcend the capabilities of a human logical designer to arrive at the logic required to sort out, say, the various letters in hand-written cursive writing where this constitutes the input to a network. Learning networks could also have importance in real time situations where some form of rapid and complex adaptation is required which is beyond the capabilities of the more orthodox methods. Because of the great difficulties involved, no attempt is made in this report at an analysis of these networks and their properties. It is solely an empirical investigation of them based on computer-simulated experiments. (Author).
Multilayer Social Networks
Author: Mark E. Dickison
language: en
Publisher: Cambridge University Press
Release Date: 2016-07-19
This book unifies and consolidates methods for analyzing multilayer networks arising from the social and physical sciences and computing.
Fuzzy Systems
Author: Constantin Volosencu
language: en
Publisher: BoD – Books on Demand
Release Date: 2022-01-12
This book presents some recent specialized works of theoretical study in the domain of fuzzy systems. Over eight sections and fifteen chapters, the volume addresses fuzzy systems concepts and promotes them in practical applications in the following thematic areas: fuzzy mathematics, decision making, clustering, adaptive neural fuzzy inference systems, control systems, process monitoring, green infrastructure, and medicine. The studies published in the book develop new theoretical concepts that improve the properties and performances of fuzzy systems. This book is a useful resource for specialists, engineers, professors, and students.