EVM Loss: A Loss Function for Training Neural Networks in Communication Systems
نویسندگان
چکیده
منابع مشابه
Introducing a New Method for Multiarea Transmission Networks Loss Allocation
Transmission loss allocation in very large networks with multiple interconnected areas or countries is investigated in this paper. The main contribution is to propose a method to calculate the amount of losses due to activity of each participant in the multi area markets. Pricing of cross-border trades in Multi area systems is often difficult since individual countries may use incompatible ...
متن کاملQuality Loss Function – A Common Methodology for Three Cases
The quality loss function developed by Genichi Taguchi considers three cases, nominal-thebest, smaller-the-better, and larger-the-better. The methodology used to deal with the larger-thebetter case is slightly different than the other two cases. This research employs a term called target-mean ratio to propose a common formula for all three cases to bring about similarity among them. The target-...
متن کاملDesign and Practical Implementation of a New Markov Model Predictive Controller for Variable Communication Packet Loss in Network Control Systems
The current paper investigates the influence of packet losses in network control systems (NCS’s) using the model predictive control (MPC) strategy. The study focuses on two main network packet losses due to sensor to controller and controller to actuator along the communication paths. A new Markov-based method is employed to recursively estimate the probability of time delay in controller to ac...
متن کاملBAYES ESTIMATION USING A LINEX LOSS FUNCTION
This paper considers estimation of normal mean ? when the variance is unknown, using the LINEX loss function. The unique Bayes estimate of ? is obtained when the precision parameter has an Inverse Gaussian prior density
متن کاملTraining Deep Neural Networks via Direct Loss Minimization
Supervised training of deep neural nets typically relies on minimizing cross-entropy. However, in many domains, we are interested in performing well on metrics specific to the application. In this paper we propose a direct loss minimization approach to train deep neural networks, which provably minimizes the application-specific loss function. This is often non-trivial, since these functions ar...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2021
ISSN: 1424-8220
DOI: 10.3390/s21041094