Artificial neural networks output layer

Figure 9-16. ArtiFicial neural network architecture with a two-layer design, compri ing Input units, a so-called hidden layer, and an output layer. The squares Inclosing the ones depict the bias which Is an extra weight (see Ref, [10 for further details),...

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. 

Fig. 7. Artificial neural network model. Bioactivities and descriptor values are the input and a final model is the output. Numerical values enter through the input layer, pass through the neurons, and are transformed into output values the connections (arrows) are the numerical weights. As the model is trained on the Training Set, the system-dependent variables of the neurons and the weights are determined.

Figure 5.3 Internal organisation of an artificial neural network. In general, there is a neuron per original variable in the input layer of neurons and all neurons are interconnected. The number of neurons in the output layer depends on the particular application (see text for details).

An artificial neural network (ANN) model was developed to predict the structure of the mesoporous materials based on the composition of their synthesis mixtures. The predictive ability of the networks was tested through comparison of the mesophase structures predicted by the model and those actually determined by XRD. Among the various ANN models available, three-layer feed-forward neural networks with one hidden layer are known to be universal approximators [11, 12]. The neural network retained in this work is described by the following set of equations that correlate the network output S (currently, the structure of the material) to the input variables U, which represent here the normalized composition of the synthesis mixture ... 

Figure 8.2 A motor neuron (a) and small artificial neural network (b). A neuron collects signals from other neurons via its dendrites. If the neuron is sufficiently activated, it sends a signal to other neurons via its axon. Artificial neural network are often grouped into layers. Data is entered through the input layer. It is processed by the neurons of the hidden layer and then fed to the neurons of the output layer. (Illustration of motor neuron from Life ART Collection Images 1989-2001 by Lippincott Williams Wilkins used by permission from SmartDraw.com.)...

Artificial neural networks often have a layered structure as shown in Figure 8.2 (b). The first layer is the input layer. The second layer is the hidden layer. The third layer is the output layer. Learning algorithms such as back-propagation that are described in many textbooks on neural networks (Kosko 1992 Rumelhart and McClelland 1986 Zell 1994) may be used to train such networks to compute a desired output for a given input. The networks are trained by adjusting the weights as well as the thresholds. 

Figure6.25 Schematicdrawingofan artificial neural network with a multilayer perceptron topology, showing the pathways from the input Xj to the output y , and the visible and hidden node layers.

Fig. 2. Structure of an artificial neural network. The network consists of three layers the input layer, the hidden layer, and the output layer. The input nodes take the values of the normalized QSAR descriptors. Each node in the hidden layer takes the weighted sum of the input nodes (represented as lines) and transforms the sum into an output value. The output node takes the weighted sum of these hidden node values and transforms the sum into an output value between 0 and 1.

Artificial neural networks (ANN s) were effectively set aside for 15 years after a 1969 study by Minksy and Papert demonstrated their failure to correctly model a simple exclusive OR (XOR) function. i The XOR function describes the result of an operation involving two bits (1 or 0). A simple OR function produces a value of 1 if either bit or both bits have a value of 1. The XOR differs from an OR function in the output of an operation on two bits of value 1. The XOR function will yield a 0 while the OR function will yield a 1. Interest in ANN s resumed in the 1980 s after modifications were made to the layering of their neurons that allowed them to overcome the XOR test as well as a wide variety of other non-linear modeling challenges. 

In parallel to the SUBSTRUCT analysis, a three-layered artificial neural network was trained to classify CNS-i- and CNS- compounds. As mentioned previously, for any classification the descriptor selection is a cmcial step. Chose and Crippen published a compilation of 120 different descriptors, which were used to calculate AlogP values as weU as drug-likeness [53, 54]. Here, 92 of the 120 descriptors and the same datasets for training and tests as for the SUBSTRUCT algorithm were used. The network consisted of 92 input neurons, five hidden neurons, and one output neuron. 

The word network in the term artificial neural network refers to the interconnections between the neurons in the different layers of each system. An example system has three layers. The first layer has input neurons, which send data via synapses to the second layer of neurons, and then via more synapses to the third layer of ouqjut neurons. More complex systems will have more layers of neurons with some having increased layers of input neurons and output neurons. The synapses store parameters called weights that manipulate the data in the calculations. 

Figure 3.10 Principal architecture of a three-layer artificial neural network (Aoyama and Ichikawa, 1991). A input layer with the number of input neurons corresponding to the number of parameters plus 1 B hidden layer with an arbitrary number of neurons C output layer with the number of output neurons corresponding to the number of categories in the respective classification problem.

Figure 10.5(b) Prediction with no variable selection. The experimental rationale was as for part (a), except that the artificial neural network was run on all 150 variables, that is, no variable selection was used. Optimization in this case resulted in having 8 nodes in the hidden layer the outputs were obtained after 10 000 epochs with an error on the calibration set of 0.01 6 out of 15 (40%) unadulterated samples and 12 out of 15 (80%) adulterated samples were predicted correctly. It may be seen that very poor separation was achieved - the two data bases do not line up on the predictive value of 0 or 1 but form a loose cloud in the middle area around 0.5, indicating that the net was unable to separate the two groups clearly. 

No chapter on modern chemometric methods would be complete without a mention of artificial neural networks (ANN). In a simple form these attempt to imitate the operation of neurons in the brain. Such networks have a number of linked layers of artificial neurons, including an input and an output layer (see Figure 8.13). The measured variables are presented to the input layer and are processed, by one or more intermediate ( hidden ) layers, to produce one or more outputs. For example, in inverse calibration, the inputs could be the absorbances at a number of wavelengths and the output could be the concentration of an analyte. The network is trained by an interactive procedure using a training set. Considering the example above, for each member of the training set the neural network predicts the concentration of the analyte. The discrepancy between the observed and predicted values... 

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