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What are the methods in Multilayer Artificial Neural Network?
An artificial neural network has a more complicated mechanism than that of a perceptron model. There are several methods in multilayer artificial neural networks which are as follows −
The network can include multiple intermediary layers between its input and output layers. Such intermediary layers are known as hidden layers and the nodes installed in these layers are known as hidden nodes. The resulting architecture is called a multilayer neural network.
In a feed-forward neural network, the nodes in one layer are linked only to the nodes in the following layer. The perceptron is a single-layer, feed-forward neural network because it has just one layer of nodes the output layer that implements complicated numerical operations. In a recurrent neural network, the links can connect nodes inside a similar layer or nodes from one layer to the prior layers.
The network can use methods of activation functions other than the sign function. Instances of multiple activation functions such as linear, sigmoid (logistic), and hyperbolic tangent functions. These activation functions enable the hidden and output nodes to make output values that are nonlinear in their input parameters.
These more complexities enable multilayer neural networks to model higher complex relationships among the input and output variables. The instances can be defined using two hyperplanes that division the input space into their specific classes.
It can understand the weights of an ANN model, it is required an efficient algorithm that assembles to the right solution when a satisfactory amount of training data is supported. One method is to consider each hidden node or output node in the network as a separate perceptron unit and to use the equal weight update formula.
This method will not operate because it can lack a priori, knowledge about the correct outputs of the hidden nodes. This creates it complex to determine the error term, (y – y’), related to each hidden node. A methodology for understanding the weights of a neural network depends on the gradient descent method is shown next.
The objective of the ANN learning algorithm is to decide a class of weights w that minimize the total sum of squared errors −
$$\mathrm{E(w)\:=\:\frac{1}{2}\displaystyle\sum\limits_{i=1}^N (Y_{i}-Y^{'}_i)^2}$$
The sum of squared errors is based on w because the predicted class y’ is a function of the weights created to the hidden and output nodes. In the cases, the output of an ANN is a nonlinear function of its parameters because of the choice of its activation functions such as sigmoid or tanh function.
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