Neural network fully connected

A feedforward neural network brings together several of these little processors in a layered structure (Figure 9). The network in Figure 9 is fully connected, which means that every neuron in one layer is connected to every neuron in the next layer. The first layer actually does no processing it merely distributes the inputs to a hidden layer of neurons. These neurons process the input, and then pass the result of their computation on to the output layer. If there is a second hidden layer, the process is repeated until the output layer is reached. 

Now we can look at the biochemical networks developed in this work and compare them to the recurrent networks discussed above. Network A (Section 4.2.1) and Network C (Section 4.2.3) are fully connected to one another, and the information flows back and forth from each neuron to all the others. This situation is very much hke the one described for recurrent neural networks, and in these cases, memory, which is a necessary to demonstrate computational power, is clearly incorporated in the networks. Network B (Section 4.2.2) is a feedforward network and thus appears to have no memory in this form. However, when we examine the processes taking place in the biochemical neuron more carefully, we can see that the enzymic reactions take into account the concentration of the relevant substrates present in the system. These substrates can be fed as inputs at any time t. However, part of them also remained from the reactions that took place at time t — and thus the enzymic system in every form is influenced by the processes that took place at early stages. Hence, memory is always incorporated. 

Figure 15 The general scheme for a fully connected two-layer neural network with four inputs...

An important hybrid approach has also developed in recent years that makes use of block-structured or modular models. These models are composed of parametric and/or nonparametric components properly connected to represent reliably the input-output relation. The model specification task for this class of models is more demanding and may utilize previous parametric and/or nonparametric modeling results. A promising variant of this approach, which derives from the general Volterra-Wiener formulation, employs principal dynamic modes as a canonical set of filters to represent a broad class of nonlinear dynamic systems. Another variant of the modular approach that has recently acquired considerable popularity but will not be covered in this review is the use of artificial neural networks to represent input-output nonlinear mappings in the form of connectionist models. These connectionist models are often fully parametrized, making this approach affine to parametric modeling, as well. 

A multilayer perceptron (MLP) is a feed-forward artificial neural network model that maps sets of input data onto a set of suitable outputs (Patterson 1998). A MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one. Except for the input nodes, each node is a neuron (or processing element) with a nonlinear activation function. MLP employs a supervised learning techruque called backpropagation for training the network. MLP is a modification of the standard linear perceptron and can differentiate data that are not linearly separable. 

Artificial neural networks consist of different types of layers. There is the input-layer, one or more hidden layers and an output layer. All these layers can consist of one or more neurons. A neuron in a particular layer is connected to all neurons in the next layer, which is why this is called a feed-forward network. In other networks the neurons might be connected otherwise. An example of a different network is a recurrent neural network where there are also links that connect neurons to other neurons in a previous layer. A fully connected network is a network in which all the neurons from one layer are connected to all neurons in the next layer. 

In this example, a fully connected network was used, i.e. every input had connections to every hidden neuron and every hidden neuron had a connection to the output neuron. It is important to realize that the number of model parameters of the neural network with three hidden neurons and one output neuron is a multiple of the number of parameters of the OE linear model, however, increasing the number of model parameters in the linear OE model does not improve the model fit. 

Figure 2 Schematic diagram of a three-layer, fully-connected, feed-forward computational neural network...

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