{"id":"W2016428245","doi":"10.1080/11762320802027869","title":"Adaptive Fuzzy‐Lyapunov Controller Using Biologically Inspired Swarm Intelligence","year":2008,"lang":"en","type":"article","venue":"Applied Bionics and Biomechanics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Swarm behaviour; Controller (irrigation); Fuzzy logic; Swarm intelligence; Lyapunov function; Control theory (sociology); Computer science; Control engineering; Artificial intelligence; Engineering; Machine learning; Control (management); Biology; Nonlinear system; Particle swarm optimization","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002009986,0.000205116,0.0002227889,0.0001133765,0.000535145,0.00005589419,0.0005096846,0.0001608304,0.000003702575],"category_scores_gemma":[0.00000712894,0.0001646514,0.00006248663,0.0007286435,0.0001339552,0.0001221516,0.0003797533,0.0001460134,0.0000319972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004597777,"about_ca_system_score_gemma":0.00008555968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002104759,"about_ca_topic_score_gemma":0.000001296478,"domain_scores_codex":[0.9986053,0.00001963534,0.0003085012,0.0005515542,0.0001931524,0.000321837],"domain_scores_gemma":[0.9992439,0.00006510959,0.0001349801,0.0003183279,0.0001034549,0.0001342059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001666311,0.0001549395,0.000005199558,0.000003394669,0.00002902695,0.000006576137,0.0001165057,0.00008615416,0.03917479,0.9168609,0.00005907579,0.04348679],"study_design_scores_gemma":[0.000535837,0.0002629209,0.0001011594,0.00001186911,0.00001554295,0.0001391951,0.0001131054,0.8039726,0.01209325,0.1768609,0.005343344,0.0005503006],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03030461,0.000521765,0.9676293,0.0004086125,0.0001127645,0.0003684425,0.00001630365,0.0001573683,0.0004808754],"genre_scores_gemma":[0.8516484,0.0006748824,0.1471221,0.0004242961,0.00005630459,0.00003192833,0.000007462133,0.000008163905,0.00002641088],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8213438,"threshold_uncertainty_score":0.671429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04479450929476107,"score_gpt":0.2412548742905895,"score_spread":0.1964603649958285,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}