k-step ahead Predictions determined by simulation of the ％ one-step ahead neural network predictor. For NNARMAX ％ models the residuals are set to zero when calculating the ％ Predictions. The Predictions are compared to the observed output. ％
標簽： ahead Predictions determined simulation
上傳時間： 2016-12-27
上傳用戶：busterman
MATSNL is a package of MATLAB M-files for computing wireless sensor node lifetime/power budget and solving optimal node architecture choice problems. It is intended as an analysis and simulation tool for researchers and educators that are easy to use and modify. MATSNL is designed to give the rough power/ lifetime Predictions based on node and application specifications while giving useful insight on platform design for the large node lifetime by providing side-by-side comparison across various platforms. The MATSNL code and manual can be found at the bottom of this page. A related list of publications describing the models used in MATSNL is posted on the ENALAB part of the 2 project at http://www.eng.yale.edu/enalab/aspire.htm
標簽： computing lifetime wireless M-files
上傳時間： 2014-01-01
上傳用戶：lnnn30
MATSNL is a package of MATLAB M-files for computing wireless sensor node lifetime/power budget and solving optimal node architecture choice problems. It is intended as an analysis and simulation tool for researchers and educators that are easy to use and modify. MATSNL is designed to give the rough power/ lifetime Predictions based on node and application specifications while giving useful insight on platform design for the large node lifetime by providing side-by-side comparison across various platforms.
標簽： computing lifetime wireless M-files
上傳時間： 2017-07-19
上傳用戶：hasan2015
When 3GPP started standardizing the IMS a few years ago, most analysts expected the number of IMS deploymentsto grow dramatically as soon the initial IMS specifications were ready (3GPP Release 5 was functionallyfrozenin the first half of 2002and completedshortly after that). While those Predictions have proven to be too aggressive owing to a number of upheavals hitting the ICT (Information and Communications Technologies) sector, we are now seeing more and more commercial IMS-based service offerings in the market. At the time of writing (May 2008), there are over 30 commercial IMS networks running live traffic, addingup to over10million IMS users aroundthe world; the IMS is beingdeployedglobally. In addition, there are plenty of ongoing market activities; it is estimated that over 130 IMS contracts have been awarded to all IMS manufacturers. The number of IMS users will grow substantially as these awarded contracts are launched commercially. At the same time, the number of IMS users in presently deployed networks is steadily increasing as new services are introduced and operators running these networks migrate their non-IMS users to their IMS networks.
標簽： Multimedia Subsystem The IMS 3G IP
上傳時間： 2020-06-01
上傳用戶：shancjb
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their Predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual Predictions with Shapley values and LIME.
標簽： interpretable-machine-learning
上傳時間： 2020-06-10
上傳用戶：shancjb
