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i) Compare RBF and MLP (ii) How do you achieve fast learning in ART 2 network.
1 Answer
written 3.6 years ago by |
Compare RBF and MLP:
RBF | MLP |
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1. RBFN is a ingle hidden layer. | 1. MLP is a multiple hidden layer. |
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2. In RBF hidden layer computation nodes are different from output nodes. | 2. MLP follows the common computational model in hidden as well as output. |
3. In RBF hidden layer is non-linear and output layer is linear. | 3. In MLP hidden layer and output layer is linear. |
4. The argument of RBF activation function computes Euclidean norm between input vector and centre. | 4. Each hidden unit computes the inner product of input vector and synaptic vector. |
5. Exponentially decaying local characteristics. | 5. Global approximation to non-linear input - output mapping. |
6. RBFN is fully connected. | 6. MLP can be partially connected. |
7. In RBFN, the hidden nodes operate differently i.e. they have different models. | 7. In MLP, the hidden nodes share a common model not necessary the same activation function. |
8. In RBF network we take differece of input vector and weight vector | 8. In MLP network we take product of input vector and weight vector. |
9. In RBF training of 1 layer at a time. | 9. In MLP training of all layer simultaneously. |
10. RBFN does faster training process. | 10. MLP is slower in training process. |
11. RBFN is slow when practically used. | 11. MLP is faster when practically used. |
(ii) How do you achieve fast learning in ART 2 network.