Implemented RBF adaptive model.

RBF_tf.py

@@ -53,3 +53,49 @@ class RBFLayer(tf.keras.layers.Layer):
         rbf_output = tf.exp(-tf.square(distances) / (2 * tf.square(self.sigmas))) 
         return rbf_output
 
+
+class RBFAdaptiveModel(tf.keras.Model):
+    """ RBF Adaptive Model using TF Subclassing API.
+
+    Outputs one control signal adaptation. Determined by output layer
+    number of neurons.
+
+    ...
+
+    Attributes
+    ----------
+    n_centers : int
+        The number of RBF centers.
+
+    Methods
+    -------
+    call(inputs):
+        TF call method to implement forward pass of model.
+    """
+    def __init__(self, n_centers):
+        """ Constructs RBF and output layers.
+
+        Parameters
+        ----------
+            n_centers : int
+                The number of RBF centers.
+        """
+        super().__init__()
+        self.rbf_layer = RBFLayer(n_centers)
+        self.output_layer = tf.keras.layers.Dense(1)
+
+    def call(self, inputs):
+        """ Implement forward pass.  
+
+        Parameters
+        ----------
+            inputs : tensor
+                The points in space to adapt with.
+
+        Returns
+        -------
+        Adapted control signal.          
+        """
+        rbf_output = self.rbf_layer(inputs)
+        control_signal = self.output_layer(rbf_output)
+        return control_signal