Application of SFG in learning algorithms of neural networks

The paper presents the application of signal flow graphs (SFG) and adjoint flow graphs (AFG) in determination the gradient vector for feedforward neural networks. The presented approach is universal and applicable in the same form irrespective of the particular structure of the network. The applicability of the method has been shown on examples of different types of neural networks: multilayer perceptron, sigma-pi network, generalized radial basis network and multilayer Volterra network. The method finds application in any gradient based learning algorithms of neural networks. Some applications of this method, concerning the prediction and identification of the nonlinear dynamic plants are presented and discussed in the paper.