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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Fishing quotas are unpleasant but efficient to control the productivity of afishing site. A popular model has a stochastic differential equation for thebiomass on which a stochastic dynamic programming or a Hamilton-Jacobi-Bellmanalgorithm can be used to find the stochastic control -- the fishing quota. Wecompare the solutions obtained by dynamic programming against those obtainedwith a neural network which preserves the Markov property of the solution. Themethod is extended to a similar multi species model to check its robustness inhigh dimension.

Performance of a Markovian neural network versus dynamic programming on a fishing control problem