73,933 research outputs found
DISCO Nets: DISsimilarity COefficient Networks
We present a new type of probabilistic model which we call DISsimilarity
COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample
from a posterior distribution parametrised by a neural network. During
training, DISCO Nets are learned by minimising the dissimilarity coefficient
between the true distribution and the estimated distribution. This allows us to
tailor the training to the loss related to the task at hand. We empirically
show that (i) by modeling uncertainty on the output value, DISCO Nets
outperform equivalent non-probabilistic predictive networks and (ii) DISCO Nets
accurately model the uncertainty of the output, outperforming existing
probabilistic models based on deep neural networks
A neural network architecture for implementation of expert systems for real time monitoring
Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered
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