Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use input-output patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a Neuro-Fuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement. © 2015 Elsevier Ltd

Search for new resonances decaying to a W or Z boson and a Higgs boson in the ℓ+ℓ−bb¯, ℓνbb¯, and νν¯bb¯ channels with pp collisions at s=13 TeV with the ATLAS detector

A search is presented for new resonances decaying to a W or Z boson and a Higgs boson in the ℓ+ℓ−bb¯, ℓνbb¯, and νν¯bb¯ channels in pp collisions at s=13 TeV with the ATLAS detector at the Large Hadron Collider using a total integrated luminosity of 3.2 fb−1. The search is conducted by looking for a localized excess in the WH/ZH invariant or transverse mass distribution. No significant excess is observed, and the results are interpreted in terms of constraints on a simplified model based on a phenomenological Lagrangian of heavy vector triplets