Hidden layer processing elements

Artificial neural networks (ANN) are computing tools made up of simple, interconnected processing elements called neurons. The neurons are arranged in layers. The feed-forward network consists of an input layer, one or more hidden layers, and an output layer. ANNs are known to be well suited for assimilating knowledge about complex processes if they are properly subjected to input-output patterns about the process. 

A CCN consists of a number of relatively simple, densely interconnected, processing elements (bone cells), with many more interconnections than cells. Operationally these cells are organized into layers an initial input, a final output and one or more intermediate or hidden layers. However such networks need not be numerically complex to be operationally complex [109]. 

A neural network consists of many processing elements joined together. A typical network consists of a sequence of layers with full or random connections between successive layers. A minimum of two layers is required the input buffer where data is presented and the output layer where the results are held. However, most networks also include intermediate layers called hidden layers. An example of such an ANN network is one used for the indirect determination of the Reid vapor pressure (RVP) and the distillate boiling point (BP) on the basis of 9 operating variables and the past history of their relationships to the variables of interest (Figure 2.56). 

The processing elements are typically arranged in layers one of the most commonly used arrangements is known as a back propagation, feed forward network as shown in Figure 7.8. In this network there is a layer of neurons for the input, one unit for each physicochemical descriptor. These neurons do no processing, but simply act as distributors of their inputs (the values of the variables for each compound) to the neurons in the next layer, the hidden layer. The input layer also includes a bias neuron that has a constant output of 1 and serves as a scaling device to ensure... 

A neural network is a system of interconnected processing elements called neurones or nodes. Each node has a number of inputs and one output, which is a function of the inputs. There are three types of neurone layers input, hidden, and output layers. Two layers communicate via a weight connection network. The nodes are connected together in complex systems, enabling comprehensive processing capabilities. The archetype neural network is of course the human brain, but there is no further resemblance between the brain and the mathematical algorithms of neural networks used today. 

Artificial neural networks are tools that allow meaning to be extracted from very large quantities of data. Neural networks (NN) are organized in the form of layers, within which there are one or more processing elements called neurons. The first layer is the input layer, and the number of neurons in this layer is equal to the munber of input parameters. The last layer is the output layer and the munber of neurons in this layer is equal to the number of output parameters to be predicted. The layers between the input and output layers are the hidden layers, consisting of a number of nem-ons to be defined in configuring the NN. Neurons in each layer receive... 

ANN simulates the eomplex fimetioning of biologieal neuron. Every eomponent of ANN bears a direct analogy to the actual constituents of biological neuron. The schematic model of an ANN is shown in Figure 2.2. The ANN is having three or more number of layers each composed of finite number of processing elements or neurons or nodes. The input layer and output layer represent the input parameters and output parameter/parameters. Therefore the number of neurons in these layers is equal with the respective nrtmber of parameters. Hidden layers, which are one or more in number, are sandwiched between the input and output layers. 

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