Merge pull request #80 from nasa/examples/param_est

Rename Param Est Example
nasa · Aug 22, 2023 · a421b92 · a421b92
2 parents 9294699 + dc1f4ce
commit a421b92
Showing 1 changed file with 3 additions and 5 deletions.
diff --git a/examples/param_est.ipynb → examples/02_param_est.ipynb b/examples/param_est.ipynb → examples/02_param_est.ipynb
@@ -5,19 +5,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Welcome to the Parameter Estimation Feature Example"
+    "# 2. Parameter Estimation"
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The goal of this notebook is to instruct ProgPy users on how to use the estimate_params feature for PrognosticModels.\n",
+    "Parameter estimation is used to tune the parameters of a general model so its behavior matches the behavior of a specific system. For example, parameters of the battery model can be tuned to configure the model to describe the behavior of a specific battery.\n",
     "\n",
-    "First some background. Parameter estimation is used to tune the parameters of a general model so its behavior matches the behavior of a specific system. For example, parameters of the battery model can be tuned to configure the model to describe the behavior of a specific battery.\n",
-    "\n",
-    "Generally, parameter estimation is done by tuning the parameters of the model so that simulation best matches the behavior observed in some available data. In ProgPy, this is done using the progpy.PrognosticsModel.estimate_params() method. This method takes input and output data from one or more runs, and uses scipy.optimize.minimize function to estimate the parameters of the model. For more information, refer to our Documentation [here](https://nasa.github.io/progpy/prog_models_guide.html#parameter-estimation)\n",
+    "Generally, parameter estimation is done by tuning the parameters of the model so that simulation (see 1. Simulation) best matches the behavior observed in some available data. In ProgPy, this is done using the progpy.PrognosticsModel.estimate_params() method. This method takes input and output data from one or more runs, and uses scipy.optimize.minimize function to estimate the parameters of the model. For more information, refer to our Documentation [here](https://nasa.github.io/progpy/prog_models_guide.html#parameter-estimation)\n",
     "\n",
     "A few definitions:\n",
     "* __`keys`__ `(list[str])`: Parameter keys to optimize\n",