Artificial neurons transfer function

Figure 9-13. Artificial neuron the signals x, are weighted (with weights IV,) and summed to produce a net signal Net. This net signal is then modified by a transfer function and sent as an output to other neurons,...

The basic model of a single artificial neuron consists of a weighted summer and an activation (or transfer) function as shown in Figure 10.20. Figure 10.20 shows a neuron in the yth layer, where... 

Fig. 44.2. An artificial neuron x,. ..Xp are the incoming signals wi... Wp are the corresponding weight factors and F is the transfer function.

A biological neuron can be active (excited) or inactive (not excited). Similarly, the artificial neurons can also have different activation status. Some neurons can be programmed to have only two states (active/inactive) as the biological ones, but others can take any value within a certain range. The final output or response of a neuron (let us call it a) is determined by its transfer function, f, which operates on the net signal (Netj) received by the neuron. Hence the overall output of a neuron can be summarised as ... 

Our choice for the non-linear system approach to PARC is the ANN. The ANN is composed of many neurons configured in layers such that data pass from an input layer through any number of middle layers and finally exit the system through a final layer called the output layer. In Fig. 4 is shown a diagram of a simple three-layer ANN. The input layer is composed of numeric scalar data values, whereas the middle and output layers are composed of artificial neurons. These artificial neurons are essentially weighted transfer functions that convert their inputs into a single desired output. The individual layer components are referred to as nodes. Every input node is connected to every middle node, and every middle node is connected to every output node. 

FIGURE 4.10 Schematic image of an artificial neuron. The input data x are calculated with their connective weight w to form the Net value of the neuron. A transfer function is applied to mimic the threshold of the biological neuron. The out value represents the outcome of the process, which is fed to another artificial neuron. 

Artificial neural networks (Hurtado 2004) are computational devices which permit the approximate calculation of outputs given an input set. The input is organized as a layer of neurons, each corresponding to one of the input variables, and the output is contained in an output layer. Intermediate, hidden layers, contain a number of neurons which receive information from the input layer and pass it on to subsequent layers. Each link in the network is associated with a weight w. The total information received by a neuron is processed by a transfer function h before being sent forward to the neurons in the next layer. For a network with a single hidden layer, the computational process can be expressed as... 

The artificial neural network (ANN) is a system imitating the operation of a biological neural network. It is composed of the set of basic elements (artificial neurons) that are mutually connected. In general, to describe the ANN operation at least three basic properties should be known namely a neuron model (transfer function), the network topology and the method of training. 

Figure 9.6. Artificial neuron or node, p, a, and w represent the input, output, and weight, respectively. n is the net input and/is the transfer function. (Reproduced from [26], by permission of John Wiley Sons, Ltd. copyright 2002.)...

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