...name implies, the neurons in multi-layer perceptron networks are organized into a number of layers: an input layer, one or more hidden layers, and an output layer. All neurons are connected to neurons in adjoining layers and communicate with each other via connection...

...feedforward, also called multilayer perceptrons with the architecture of interconnected neurons in an input layer, one or more ..hidden" layers, and an output layer (see Fig. 5). Number of processing units corresponds to the desired model inputs and outputs. Optimal...

...as a generalization of the single-layer perceptron, by connecting the single layers. Typically, the network consists of an input layer, one or more hidden layers, and an output layer. The input signal propagates through the network in a forward direction. The first hidden layer is fed...

...multi-layer perceptron - MLP) network is the most commonly used neural network for modelling. A MLP typically consists of an input layer, one or more hidden layers, and an output layer [8]. Many workers have demonstrated the power of the MLP network and research indicates that it is...

...learning is carried out when a set of training patterns is propagated through a network consisting of an input layer, one or more hidden layers and an output layer. Each layer has its corresponding units and weight connections. The topology of a BP network is illustrated...

...MLP is a feed-forward network built up of perceptron -type neurons, arranged in layers. An MLP has an input layer, one or more hidden layers and an output layer. 1n Figure 5 a single hidden layer multi-input - multi-output MLP is shown. An MLP is a fully connected...

...is developed with multi-layer perceptron (MLP). This is a unidirectional model of neural net made up of an input layer, one or more hidden layers and an output layer. The activation function of the neurons belonging to the hidden layers must be nonlinear so that the...

...applications are successful implementations of MLPs. The architecture of the MLP is completely defined by an input layer, one or more hidden layers, and an output layer. Each layer consists of at least one neuron. The input vector is processed by the MLP in a forward direction,...

...learning model, due to its simplicity. The architecture of BP networks, depicted in Figure 1, includes an input layer, one or more hidden layers, and an output layer. The nodes in each layer are connected to each node in the adjacent layer. Notably, Hecht-Nielsen proved...

...(Rulmelhart, Hinton & Williams, 1986), which is used to train a MLP. In structure this network has an input layer, one or more hidden layers, and an output layer. Unsupervised learning is dominated by applications of the self-organising feature map (Kohonen, 1982)....