What is the difference between multi-layer perceptron and generalized feed forward neural network? I'm reading this paper:An artificial neural network model for rainfall forecasting in Bangkok, Thailand. The author created 6 models, 2 of which have the following architecture: 

model B: Simple multilayer perceptron with Sigmoid activation function and 4 layers in which the number of nodes are: 5-10-10-1, respectively.

model C: Generalized feedforward with Sigmoid activation function and 4 layers in which the number of nodes are: 5-10-10-1, respectively.

In the Results and discussion section of the paper, the author concludes that :

Model C enhanced the performance compared to Model A and B. This suggests that the generalized feedforward network performed better than the simple multilayer perceptron network in this study

Is there a difference between these 2 architectures?
Well you missed the diagram they provided for the GFNN. Here is the diagram from their page:



Clearly you can see what the GFNN does, unlike MLP the inputs are applied to the hidden layers also. While in MLP the only way information can travel to hidden layers is through previous layers, in GFNN the input information is directly available to the hidden layers.

I might add this type of connections are used in ResNet CNN, which increased its performance dramatically compared to other CNN architectures.