diff --git a/201 Metadata/My Library.bib b/201 Metadata/My Library.bib
index c2191f42c..686fe9a2d 100644
--- a/201 Metadata/My Library.bib	
+++ b/201 Metadata/My Library.bib	
@@ -65,6 +65,23 @@
   file = {/home/danesabo/Zotero/storage/6T7S8SNG/Adiego et al. - 2014 - Bringing Automated Model Checking to PLC Program D.pdf}
 }
 
+@article{agarwalSystematicClassificationNeuralnetworkbased1997,
+  title = {A Systematic Classification of Neural-Network-Based Control},
+  author = {Agarwal, M.},
+  date = {1997-04},
+  journaltitle = {IEEE Control Systems Magazine},
+  volume = {17},
+  number = {2},
+  pages = {75--93},
+  issn = {1941-000X},
+  doi = {10.1109/37.581297},
+  url = {https://ieeexplore.ieee.org/document/581297},
+  urldate = {2025-04-07},
+  abstract = {Successful industrial applications and favorable comparisons with conventional alternatives have motivated the development of a large number of schemes for neural-network-based control. Each scheme is usually composed of several independent functional features, which makes it difficult to identify precisely what is new in the scheme. Help from available overviews is therefore often inadequate, since they usually discuss only the most important overall schemes. This work breaks the available schemes down to their essential functional features and organizes the latter into a multi-level classification. The classification reveals that similar schemes often get placed in different categories, fundamentally different features often get lumped into a single category, and proposed new schemes are often merely permutations and combinations of the well-established fundamental features. The classification has two main sections: neural network only as an aid; and neural network as controller.},
+  keywords = {Computer networks,Concurrent computing,Control systems,Convergence of numerical methods,Electrical equipment industry,Industrial control,Neural networks,Proposals,Stability analysis,Taxonomy},
+  file = {/home/danesabo/Zotero/storage/XFTP8R8H/581297.html}
+}
+
 @report{agencyAssessmentManagementAgeing2007,
   type = {Text},
   title = {Assessment and {{Management}} of {{Ageing}} of {{Major Nuclear Power Plant Components Important}} to {{Safety}}: {{PWR Vessel Internals}}},
@@ -4836,7 +4853,7 @@ for defect classification of TFT–LCD panels.pdf}
   urldate = {2025-04-07},
   isbn = {978-1-4471-1076-7 978-1-4471-0345-5},
   keywords = {Adaptive control,artificial intelligence,complexity,Control,Control Engineering,development,genetic algorithms,Identification,learning,model,Modelling,Neural Networks,Nonlinear control,Wavelets},
-  file = {/home/danesabo/Zotero/storage/UNUL6UKU/Liu - 2001 - Nonlinear Identification and Control.pdf}
+  file = {/home/danesabo/Zotero/storage/GB2UZ6SZ/(Advances in Industrial Control) G. P. Liu BEng, MEng, PhD (auth.) - Nonlinear Identification and Control_ A Neural Network Approach-Springer-Verlag London (2001).pdf;/home/danesabo/Zotero/storage/UNUL6UKU/Liu - 2001 - Nonlinear Identification and Control.pdf}
 }
 
 @incollection{liuNonlinearPredictiveNeural2001,