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One-layer network

Note that the functional link network can be treated as a one-layer network, where additional input data are generated off line using nonlinear transformations. The learning procedure for one-layer is easy and fast. Figure 19.24 shows an XORproblem solved using functional link networks. Note that when the functional link approach is used, this difficult problem becomes a trivial one. The problem with the functional link network is that proper selection of nonlinear elements is not an easy task. In many practical cases, however, it is not difficult to predict what kind of transformation of input data may linearize the problem, and so the functional link approach can be used. [Pg.2049]

In spite of being actually partitioned into L+1 layers, a neural network with such an architecture is conventionally called an L-layer network (due to the fact that signals undergo transformations only in the layers of hidden and output neurons, not in the input layer). In particular, a one-layer network is a layered neural network without hidden neurons, whereas a two-layer network is a neural network in which only connections from input to hidden neurons and from hidden to output neurons are possible. [Pg.83]

The most prominent example of one-layer networks are perceptrons — the earliest representative of artificial neural networks in the sense... [Pg.83]

Artificial neural networks can be divided into two main categories one-layer and multilayer networks. A typical one-layer network is the Kohonen network. ... [Pg.1300]

In a backpropagation network each neurone of a layer is coimected to each neurone in the previous and in the next layer. Connections spanning over one layer are forbidden in this architecture. [Pg.464]

A counter-propagation network is a method for supervised learning which can be used for prediction, It has a two-layer architecture where each netiron in the upper layer, the Kohonen layer, has a corresponding netiron in the lower layer, the output layer (sec Figure 9-21). A trained counter-propagation network can be used as a look-up tabic a neuron in one layer is used as a pointer to the other layer. [Pg.459]

The data set was then sent into a counter-propagation (CPG) network consisting of 13 X 9 neurons with 10 layers (one for each descriptor) in the input block and one layer in the output block (figure 10.1-9), with the output values having nine different values corresponding to the nine different MOA. [Pg.508]

Rather than making this statement, one should consider first whether the representation of the Y-variablc is appropriate. What wc did here was to take categorical information as a quantitative value. So if wc have, for instance, a vector of class 1 and one of c lass 9 falling into the same neuron, the weights of the output layer will be adapted to a value between 1 and 9, which docs not make much sense. Thus, it is necessary to choose another representation with one layer for each biological activity. The architecture of such a counter-propagation network is shown in Figure 10.1 -11. Each of the nine layers in the output block corresponds to a different MOA. [Pg.509]

Consider a three layer network. Let the input layer be layer one ( = 1), the hidden layer be layer two ( = 2) and the output layer be layer three ( = 3). The baek-propagation eommenees with layer three where dj is known and henee 8j ean be ealeulated using equation (10.69), and the weights adjusted using equation (10.71). To adjust the weights on the hidden layer = 2) equation (10.69) is replaeed by... [Pg.353]

A neural network consists of many neurons organized into a structure called the network architecture. Although there are many possible network architectures, one of the most popular and successful is the multilayer perceptron (MLP) network. This consists of identical neurons all interconnected and organized in layers, with those in one layer connected to those in the next layer so that the outputs in one layer become the inputs in the subsequent... [Pg.688]

Neurons are not used alone, but in networks in which they constitute layers. In Fig. 33.21 a two-layer network is shown. In the first layer two neurons are linked each to two inputs, x, and X2- The upper one is the one we already described, the lower one has w, = 2, W2 = 1 and also 7= 1. It is easy to understand that for this neuron, the output )>2 is 1 on and above line b in Fig. 33.22a and 0 below it. The outputs of the neurons now serve as inputs to a third neuron, constituting a second layer. Both have weight 0.5 and 7 for this neuron is 0.75. The output yfi j, of this neuron is 1 if E = 0.5 y, + 0.5 y2 > 0.75 and 0 otherwise. Since y, and y2 have as possible values 0 and 1, the condition for 7 > 0.75 is fulfilled only when both are equal to 1, i.e. in the dashed area of Fig. 33.22b. The boundary obtained is now no longer straight, but consists of two pieces. This network is only a simple demonstration network. Real networks have many more nodes and transfer functions are usually non-linear and it will be intuitively clear that boundaries of a very complex nature can be developed. How to do this, and applications of supervised pattern recognition are described in detail in Chapter 44 but it should be stated here that excellent results can be obtained. [Pg.234]

The general stmcture is shown in Fig. 44.9. The units are ordered in layers. There are three types of layers the input layer, the output layer and the hidden layer(s). All units from one layer are connected to all units of the following layer. The network receives the input signals through the input layer. Information is then passed to the hidden layer(s) and finally to the output layer that produces the response of the network. There may be zero, one or more hidden layers. Networks with one hidden layer make up the vast majority of the networks. The number of units in the input layer is determined by p, the number of variables in the (nxp) matrix X. The number of units in the output layer is determined by q, the number of variables in the inxq) matrix Y, the solution pattern. [Pg.662]

One layer of input nodes and another of output nodes form the bookends to one or more layers of hidden nodes Signals flow from the input layer to the hidden nodes, where they are processed, and then on to the output nodes, which feed the response of the network out to the user. There are no recursive links in the network that could feed signals from a "later" node to an "earlier" one or return the output from a node to itself. Because the messages in this type of layered network move only in the forward direction when input data are processed, this is known as a feedforward network. [Pg.27]

A feedforward neural network brings together several of these little processors in a layered structure (Figure 9). The network in Figure 9 is fully connected, which means that every neuron in one layer is connected to every neuron in the next layer. The first layer actually does no processing it merely distributes the inputs to a hidden layer of neurons. These neurons process the input, and then pass the result of their computation on to the output layer. If there is a second hidden layer, the process is repeated until the output layer is reached. [Pg.370]

We recall that AI tools need a memory—where is it in the neural network There is an additional feature of the network to which we have not yet been introduced. The signal output by a neuron in one layer is multiplied by a connection weight (Figure 10) before being passed to the next neuron, and it is these connection weights that form the memory of the network. [Pg.370]

The response range of the local environment to the excited Trp-probe is mainly within 10 A because the dipole-dipole interaction at 10 A to that at —3.5 A of the first solvent shell drops to 4.3%. This interaction distance is also confirmed by recent calculations [151]. Thus, the hydration dynamics we obtained from each Trp-probe reflects water motion in the approximately three neighboring solvent shells. About seven layers of water molecules exist in the 50-A channel, and we observed three discrete dynamic structures. We estimated about four layers of bulk-like free water near the channel center, about two layers of quasi-bound water networks in the middle, and one layer of well-ordered rigid water at the lipid interface. Because of lipid fluctuation, water can penetrate into the lipid headgroups, and one more trapped water layer is probably buried in the headgroups. As a result, about two bound-water layers exist around the lipid interface. The obtained distribution of distinct water structures is also consistent with —15 A of hydration layers observed by X-ray diffraction studies from White and colleagues [152, 153], These discrete water stmctures in the nanochannel are schematically shown in Figure 21, and these water molecules are all in dynamical equilibrium. [Pg.108]

Figure 1. Sketch of an icelike cluster around a selected water molecule. In this structure, a molecule from a sublayer (dotted lines) of one layer (delimited with full lines) is connected via hydrogen bonds with three molecules of the other sublayer of the same layer (only two bonds are drawn in the picture it should be noted that the molecules drawn are not in the same plane) and with one water molecule from an adjacent layer. The first four neighbors are located at the vertexes of a tetrahedron. The projection of the position of the water molecules from one layer in the plane of the surface (denoted in the text as xy) is a hexagonal network. Each icelike cluster involves 26 molecules around the selected molecule (marked) they are located in three water layers a central and the two adjacent ones. Figure 1. Sketch of an icelike cluster around a selected water molecule. In this structure, a molecule from a sublayer (dotted lines) of one layer (delimited with full lines) is connected via hydrogen bonds with three molecules of the other sublayer of the same layer (only two bonds are drawn in the picture it should be noted that the molecules drawn are not in the same plane) and with one water molecule from an adjacent layer. The first four neighbors are located at the vertexes of a tetrahedron. The projection of the position of the water molecules from one layer in the plane of the surface (denoted in the text as xy) is a hexagonal network. Each icelike cluster involves 26 molecules around the selected molecule (marked) they are located in three water layers a central and the two adjacent ones.
Another division of neural networks corresponds to the number of layers a simple perception has only one layer (Minski and Papert, 1969), whereas a multilayer perception that has more than one layei (Hertz et al., 1991). This simple differentiation means that network architecture is very important and each application requires its own design. To get good results one should store in the network as much knowledge as possible and use criteria for optimal network architecture as the number of units, the number of connections, the learning time, cost and so on. A genetic algorithm can be used to search the possible architectures (Whitley and Hanson, 1989). [Pg.176]


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Four-layer flow network for one chemical

Layered network

Network layer

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