In the second part, the process of pattern formation in Hopfield linear associators, realized as single-layer neural networks with Hebbian weight adjustment rules, is discussed. The main result of the paper is then presented, according to which pattern formation in Hopfield linear associators is a solution to a form of Simultaneous Byzantine Agreement. In conclusion, it is argued that such associative memory solutions to interactive consistency problems in generalized transaction processing systems may finally prove viable, despite decades of neglect due to inavailability or prohibitive expense of sufficient processing power for their large-scale implementation. In this paper we present a generalization of these methods to the fusion of dependent estimates and also to discrete stochastic systems determined by difference equation. The proposed fusion methods allow fully parallel processing of information and fit in with multisensor environment.
Examples demonstrate the accuracy and efficiency of the proposed fusion methods.
It supports important object-oriented features such as object identity, complex objects, classes, class hierarchies, multiple inheritance with overriding and blocking, and schema definition. It also supports structured values such as functor objects and sets, providing powerful mechanisms for representing both partial and complete information about sets. It provides a uniform declarative language for defining, manipulating and querying databases. These characteristics of consciousness are correlated with materialistic background processes, except qualia which have, as it is argued, no obvious connection with naturalistic explanatory framework.