Example of a multilayer feed-forward neural network
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Multilayer Feedforward Neural Networks
A multilayer feedforward neural network is an interconnection of perceptrons in which data and calculations flow in a single direction, from the input data to the outputs. The number of layers in a neural network is the number of layers of perceptrons. The simplest neural network is one with a single input layer and an output layer of perceptrons. The network in Figure 13-7 illustrates this type of network. Technically, this is referred to as a one-layer feedforward network with two outputs because the output layer is the only layer with an activation calculation.
A multilayer feedforward neural network is an interconnection of perceptrons in which data and calculations flow in a single direction, from the input data to the outputs. The number of layers in a neural network is the number of layers of perceptrons. The simplest neural network is one with a single input layer and an output layer of perceptrons. The network in Figure 13-7 illustrates this type of network. Technically, this is referred to as a one-layer feedforward network with two outputs because the output layer is the only layer with an activation calculation.
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A Neural networks (ANN) are non-linear statistical data modeling tools that tries to simulate the functions of biological neural networks. It consists of interconnected collection of simple processing elements or artificial neurons and processes information in a connectionist approach to computation (Han & Kamber, 2001; Stuart et al., 2004). ANN is generally considered to be an adaptive system that changes its structure in response to external or internal information that flows through the network during the learning phase.
Example of multilayer feed forward neural network (MFFNN). In MFFNN nodes represent neurons are denoted by circle, edges represent connections
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