Artificial neuron networks

Corma, A., Serra, J. M., Argente, E., Botti, V., Valero, S., Application of artificial neuronal networks to combinatorial catalysis modeling and predicting ODHE catalysts, ChemPhysChem 2002, 3, 939-945. 

Figure 5.13 Layers of units and connection links in an artificial neuronal network. Ij-ij.- input neurons, h-hj hidden neurons, b, b exit and output bias neurons, w,2hi weight of transmission, ij-h, x.-Xji input process variables and y output...

F. Blayo, M. Verleysen, Artificial Neuronal Networks, PUF, Paris, 1996. 

Chemometrics is a branch of science and technology dealing with the extraction of useful information from multidimensional measurement data using statistics and mathematics. It is applied in numerous scientific disciplines, including the analysis of food [313-315]. The most common techniques applied to multidimensional analysis include principal components analysis (PCA), factor analysis (FA), linear discriminant analysis (LDA), canonical discriminant function analysis (DA), cluster analysis (CA) and artificial neurone networks (ANN). 

In turn, general model of artificial neuron network takes Figure 5. 

For the PCA and PLS-DA, sparse analyses perform a selection from automatic variables. More recently, more complex methods of automatic learning from data mining have been applied to metabolomic data. Decision trees aid the automatic selection of discriminant variables, supply a simple representation of the decision model (the tree) and constitute an exploratory technique to understand complex metabolic profiles. The artificial neuron network was successfully used to classify chemical profiles and is becoming one of the most popular methods for understanding patterns. Data visualization and interactivity are now used to visualize metabolomic data in order to facilitate the interpretation of complex data-sets. XCMS online [GOW 14] offers cloud-plots, PCA and interactive heatmaps (i.e. the heatmaps are graphical representations of correlation matrices). These two types of visualization help the user personalize the display and easily select the most interesting compounds. 

The nervous systems and especially the brains of animals and humans work very fast, efficiently, and highly in parallel. They consist of networked neurons which work together and interchange signals with one another. This section describes the functionality of a biological neuron and explains the model of an artificial neuron. 

Artificial Neural Networks (ANNs) are information processing imits which process information in a way that is motivated by the functionality of the biological nervous system. Just as the brain consists of neurons which are connected with one another, an ANN comprises interrelated artificial neurons. The neurons work together to solve a given problem. 

Artificial Neural Networks (ANNs) attempt to emulate their biological counterparts. McCulloch and Pitts (1943) proposed a simple model of a neuron, and Hebb (1949) described a technique which became known as Hebbian learning. Rosenblatt (1961), devised a single layer of neurons, called a Perceptron, that was used for optical pattern recognition. 

Aqueous solubility is selected to demonstrate the E-state application in QSPR studies. Huuskonen et al. modeled the aqueous solubihty of 734 diverse organic compounds with multiple linear regression (MLR) and artificial neural network (ANN) approaches [27]. The set of structural descriptors comprised 31 E-state atomic indices, and three indicator variables for pyridine, ahphatic hydrocarbons and aromatic hydrocarbons, respectively. The dataset of734 chemicals was divided into a training set ( =675), a vahdation set (n=38) and a test set (n=21). A comparison of the MLR results (training, r =0.94, s=0.58 vahdation r =0.84, s=0.67 test, r =0.80, s=0.87) and the ANN results (training, r =0.96, s=0.51 vahdation r =0.85, s=0.62 tesL r =0.84, s=0.75) indicates a smah improvement for the neural network model with five hidden neurons. These QSPR models may be used for a fast and rehable computahon of the aqueous solubihty for diverse orgarhc compounds. 

A more recently introduced technique, at least in the field of chemometrics, is the use of neural networks. The methodology will be described in detail in Chapter 44. In this chapter, we will only give a short and very introductory description to be able to contrast the technique with the others described earlier. A typical artificial neuron is shown in Fig. 33.19. The isolated neuron of this figure performs a two-stage process to transform a set of inputs in a response or output. In a pattern recognition context, these inputs would be the values for the variables (in this example, limited to only 2, X and x- and the response would be a class variable, for instance y = 1 for class K and y = 0 for class L. 

The structural unit of artificial neural networks is the neuron, an abstraction of the biological neuron a typical biological neuron is shown in Fig. 44.1. Biological neurons consist of a cell body from which many branches (dendrites and axon) grow in various directions. Impulses (external or from other neurons) are received through the dendrites. In the cell body, these signals are sifted and integrated. 

In the human brain, it is the combined efforts of many neurons acting in concert that creates complex behavior this is mirrored in the structure of an ANN, in which many simple software processing units work cooperatively. It is not just these artificial units that are fundamental to the operation of ANNs so, too, are the connections between them. Consequently, artificial neural networks are often referred to as connectionist models. 

A feedforward network, the type most commonly used in chemistry, is constructed from several artificial neurons (Figure 6), which are joined together to form a single processing unit. The operation of each artificial... 

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).

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