Multiple layer network

A different set of inputs, for example IT = [.5,. 1,. 1], would have yielded an output of 0 (IT is the transpose of vector I). The same principle of information feed-forward, weighted sums and transformation applies with multiple units in each layer and, indeed, with multiple layers. Multiple layered networks will be discussed in the next chapter. 

Multiple Layer Networks are ANNs designed in multiple tiers, each of which processes different information that have to be correlated among one another. 

Neural Network Architecture for EMG Classification BP is based on the generalized form of Widrow-Hoff learning rule to multiple-layer network and nonlinear differentiable transfer function. Here, the input vectors and corresponding target vectors are used to train the neural network until it can approximate a function or associate input vectors with... 

Usually, back-propagation is chosen as the learning process of the ANN. Back-propagation is the generalization of the Widrow-Holf learning rule to multiple-layer networks and nonlinear differentiable transfer functions. The governing equations of the process are presented below. 

A crystal of covalent solid can be considered as a single molecule held together as an extensive network of covalent bonds, with no individually distinguishable small molecules. Examples are graphite as multiple layers of two-dimensional networks. 

Counterpropagation (CPG) Neural Networks are a type of ANN consisting of multiple layers (i.e., input, output, map) in which the hidden layer is a Kohonen neural network. This model eliminates the need for back-propagation, thereby reducing training time. 

Fig. 9.3 Cartoon of the fabrication of crosslinked layers. The functional unit (i. e. the semi-conductive unit) is shown in green, the reactive unit in red. The spacer between them is shown as a black line. The material is dissolved in a suitable solvent, spin coated on the top of the substrate, and finally cured to yield an insoluble polymer network (red line). Multiple-layer structures are obtained by repeated deposition and curing.

BP network is divided into the input layer, hidden layer and output layer three layers and the full link of layers structure. Output layer can have multiple points (neurons). Hidden layer can be separated into multiple layers. If the number of hidden layer units is free to design, then uses three S-shaped, which can approach to any continuous function in arbitrary precision. Choose 3 layer networks (hidden layer is the first layer) will not affect the accuracy of the network, but also can improve the speed of the network. 

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. 

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