English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

There are two parameter tuning algorithms, time update and measurement update algorithms for parameter estimation of Kalman filter. Two learning methods for parameter estimation of Kalman filter are proposed based on RLS (Recursive Least Square) method. One is the method without measurement update algorithm (RLS1) . The other one is the method without both time and measurement update algorithms (RLS2) . The methods are applied to the time series data of DMSP/SSM/I data with a plenty of missing data. It is found that the proposed RLS2 method shows smooth and fast convergence in learing process in comparison to the RLS1.

Prediction Methods for Time Series Data with Many Missing Data Based on RLS Method.