Neural 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. 

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. 

A number of researchers have tried training neural networks to achieve color constancy. A neural network basically consists of a set of nodes connected by weights (McClelland and Rumelhart 1986 Rumelhart and McClelland 1986 Zell 1994). Artificial neural networks are an abstraction from biological neural networks. Figure 8.2 shows a motor neuron in (a) and a network of eight artificial neurons on the right. A neuron may be in one of... 

The field of artificial neural networks is a new and rapidly growing field and, as such, is susceptible to problems with naming conventions. In this book, a perceptron is defined as a two-layer network of simple artificial neurons of the type described in Chapter 2. The term perceptron is sometimes used in the literature to refer to the artificial neurons themselves. Perceptrons have been around for decades (McCulloch Pitts, 1943) and were the basis of much theoretical and practical work, especially in the 1960s. Rosenblatt coined the term perceptron (Rosenblatt, 1958). Unfortunately little work was done with perceptrons for quite some time after it was realized that they could be used for only a restricted range of linearly separable problems (Minsky Papert, 1969). 

A network is composed of units or simple named nodes, which represent the neuron bodies. These units are interconnected by links that act like the axons and dendrites of their biological counterparts. A particular type of interconnected neural net is shown in Fig. 5.12. In this case, it has one input layer of three units (leftmost circles), a central or hidden layer (five circles) and one output (exit) layer (rightmost) unit. This structure is designed for each particular application, so the number of the artificial neurons in each layer and the number of the central layers is not a priori fixed. 

Artificial neural networks try to mimic the behaviour and structure of the brain. As such, they are constructed by basic processing units with multiple inputs and a single output called artificial neurons. 

An artificial neural network (ANN), usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist... 

The term neural network was traditionally used to refer to a network or circuit of biological neurons (Hopfield 1982). Modem usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus, the term has two distinct usages ... 

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... 

The artificial neural network [26] is a kind of adaptive system, which is composed of many neurons and used to abstractly simplify and simulate human brain activity. The artificial neuron as shown in Fig. 2.4 can be used as a simple processor that can deal with the arrival signals by weighted sum as ... 

Neural network has been widely used in fields of function approximation, pattern recognition, image dealing, artificial intelligence, optimization and so on [26, 102]. Multilayer feed forward artificial neural network is a major type of the neural network which is connected by input layer, one or more output layers and hidden layers in a forward way. Each layer is composed of many artificial neurons. The output of previous layer neurons is the input of the next layer as shown in Fig. 2.6. 

Natural muscles are controlled by neurons and network of neurons. We can imagine artificial neurons and network of artificial neurons as well. Artificial muscles with motor proteins are studied and attract attention[79]. One direction is to develop deformable machine with real motor proteins, actins and myosins, and neurons. Another direction is to develop neural network software to control distributed artificial muscles. The author has been developing open brain simulator which can emulate the activities of human nervous system for estimating internal state of human through external observation [231]. Such software is also applicable to control artificial muscle systems, which is implemented on the personal robots and humanoid robots in the future. 

The ANNs were developed in an attempt to imitate, mathematically, the characteristics of the biological neurons. They are composed by intercoimected artificial neurons responsible for the processing of input-output relationships, these relationships are learned by training the ANN with a set of irqmt-output patterns. The ANNs can be used for different proposes approximation of functions and classification are examples of such applications. The most common types of ANNs used for classification are the feedforward neural networks (FNNs) and the radial basis function (RBF) networks. Probabilistic neural networks (PNNs) are a kind of RBFs that uses a Bayesian decision strategy (Dehghani et al., 2006). 

Basic unit of a neural network is the artificial neuron, shown in Figure 21.1. An artificial neuron has inputs, denoted as x, and outputs, denoted as y. Information inputs are weighted by a value (w) corresponding to the specificity of each neuron input [12]. 

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. 

Whenever talking about ANNs it is wise to stress first that in the phrase neural network the emphasis is on the word network rather than on the word neural. The meaning of this remark points to the fact that the way the artificial neurons are connected or networked together is much more important than the way each neuron performs its simple operation for which it is designed. 

The natural neural network is such an incredibly complex creation that it would be futile to even attempt to manufacture an exact copy. However, it is possible to create a biologically inspired empirical model containing many densely linked nonlinear processing units (called artificial neurons). The artificial neuron carries out the conversion (in general, nonlinear) of input vector U into output value Y (approximation of the representation being the basis of empirical models) in a manner similar to that of the brain neuron (Fig. 3.5). 

There are many possibilities to connect artificial neurons into a network and to direct the flow of signals in the network. For this reason, we can distinguish many kinds of artificial neural networks, each of which has its own method for selecting the weights (learning). The most basic types of neural networks are ... 

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. 

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