Recently,　cooperative　recurrent　neural　networks　for　solving　three　linearly　constrained　L1　estimation　problems　were　developed　and　applied　to　linear　signal　and　image　models　under　non-Gaussian　noise　environments.　For　wide　applications,　this　paper　proposes　a　compact　cooperative　recurrent　neural　network　(CRNN)　for　calculating　general　constrained　L1　norm　estimators.　It　is　shown　that　the　proposed　CRNN　converges　globally　to　the　constrained　L1　norm　estimator　without　any　condition.　The　proposed　CRNN　includes　three　existing　CRNNs　as　its　special　cases.　Unlike　the　three　existing　CRNNs,　the　proposed　CRNN　is　easily　applied　and　can　deal　with　the　nonlinear　elliptical　sphere　constraint.　Moreover,　when　computing　the　general　constrained　L1　norm　estimator,　the　proposed　CRNN　has　a　fast　convergence　speed　due　to　low　computational　complexity.　Simulation　results　confirm　further　the　good　performance　of　the　proposed　CRNN.　©　2009　IEEE.