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Web10/27/2004 3 RBF Architecture • RBF Neural Networks are 2-layer, feed-forward networks. • The 1st layer (hidden) is not a traditional neural network layer. • The function of the 1st layer is to transform a non-linearly separable set of input vectors to a linearly separable set. • The second layer is then a simple feed-forward layer (e.g., of WebJan 10, 2024 · Calculate dot product of RBF and W and select an index of maximum value; Implementation of theory in Python. First, we have to define the required functions that will be used in RBFNN. The modified “kmeans” function returns the cluster centers as well as the standard deviation of the clusters. 2. Web2. RBF and MLP belong to a class of neural networks called feed-forward networks. Hidden layer of RBF is different from MLP. It performs some computations. Each hidden unit act as a point in input space and activation/output for any instance depends on the distance between that point (Hidden Unit) and instance (Also a point in space). earth cady shoes