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--- knowledge_base:professional:battery [2025/07/08 10:02] – [Modeling from Advanced BMS Work Shop AAC 2025] Normal User
+++ knowledge_base:professional:battery [2025/07/08 15:40] (current) – [Modeling from Advanced BMS Work Shop AAC 2025] Normal User
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   * SPM -> ESPM
   * {{ :knowledge_base:professional:acc-plett-advanced_battery_management-perspectives_on_the_role_of_machine_learning.zip |}}
+==== Raw Notes ====
+  * PCA - Principal Component Analysis (PCA) and neural networks are powerful tools in data analysis, and they can be used together in various ways. PCA is often used as a preprocessing step for neural networks, particularly with high-dimensional data, to reduce dimensionality and improve model performance. Additionally, neural networks can be designed to perform PCA-like operations, effectively learning the principal components during training
+  * Sys ID - surrogate AI model types and how to choose - simple feedforward, long-short (LSTM) memory model. Delayed input...
+  * Model Conversion, Training methods - PyTorch, TensorFlow, MATLA
+  * RNN vs LSTM vs GRU vs Transformers - https://www.geeksforgeeks.org/deep-learning/rnn-vs-lstm-vs-gru-vs-transformers/
+  * Virtual sensor - How to identify if the import has correlation to output to reduce the order of the system - perturbation sensitivity analysis.
+  * Imitation learning
+  * NLP/QP solving for MPC is expensive
+  * Neural state space model
+  * Reinforcement learning (RL)
+  * Pruning and projection (structure compression), quantization (data compression) to deploy for low memory low computing power applications.