{"id":"W2111010113","doi":"10.1109/ijcnn.2007.4371444","title":"A Neural Network-based Learning Controller for Micro-sized Object Micromanipulation","year":2007,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","topic":"Mechanical and Optical Resonators","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Control theory (sociology); Controller (irrigation); Artificial neural network; Compensation (psychology); Object (grammar); Computer science; Nonlinear system; Novelty; Bounded function; Lyapunov function; Artificial intelligence; Scaling; Control engineering; Control (management); Mathematics; Engineering; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002639473,0.0003959462,0.000357729,0.0001820501,0.0003006,0.0004525776,0.0008471358,0.0006385649,0.001814317],"category_scores_gemma":[0.0005106175,0.0001495374,0.0001968917,0.0001909457,0.0002962033,0.0004239175,0.0003492445,0.0006032648,0.0003728097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004322737,"about_ca_system_score_gemma":0.0005296052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002687723,"about_ca_topic_score_gemma":0.003106702,"domain_scores_codex":[0.9997974,0.00001907979,0.00001051945,0.00006080581,0.00009119149,0.00002110986],"domain_scores_gemma":[0.9998583,0.00003564995,0.00002131945,0.000009779243,0.0000661374,0.000008815301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002567278,0.0002337598,0.0006905285,0.0004210348,0.00010193,0.0002988536,0.0001098973,0.3789973,0.1163996,0.01191738,0.003552442,0.4870206],"study_design_scores_gemma":[0.00003152568,0.0001457528,0.000371533,0.00001535916,0.00002156905,0.00009089867,0.000006992954,0.9818486,0.01218704,0.0009588245,0.004306177,0.00001562736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02179005,0.001107825,0.9680561,0.0002641136,0.0002684383,0.00008766098,0.00003651164,0.000984197,0.007405131],"genre_scores_gemma":[0.8280368,0.0007395837,0.1584448,0.0003404263,0.0001228308,0.0002435472,0.00008101328,0.00004241088,0.01194858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002687723,"threshold_uncertainty_score":0.006069481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05715138330071302,"score_gpt":0.3197085136887319,"score_spread":0.2625571303880189,"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."}}