Echo　state　network　(ESN),　a　novel　recurrent　neural　network,　has　a　randomly　and　sparsely　connected　reservoir.　Since　the　reservoir　size　is　very　large,　the　collinearity　problem　may　exist　in　the　ESN.　To　address　this　problem　and　get　a　sparse　architecture,　an　adaptive　lasso　echo　state　network　(ALESN)　is　proposed,　in　which　the　adaptive　lasso　algorithm　is　used　to　calculate　the　output　weights.　The　ALESN　combines　the　advantages　of　quadratic　regularization　and　adaptively　weighted　lasso　shrinkage;　furthermore,　it　has　the　oracle　properties　and　can　deal　with　the　collinearity　problem.　Meanwhile,　to　obtain　the　optimal　model,　the　selection　of　tuning　regularization　parameter　based　on　modified　Bayesian　information　criterion　is　proposed.　Simulation　results　show　that　the　proposed　ALESN　has　better　performance　and　relatively　uniform　output　weights　than　some　other　existing　methods.