Input weight

All inputs to the control loop (changes in set-point or disturbances) are generically represented by V(s). The input V(s) is found by passing a mathematically bounded normalized input V (s) through a transfer function block lV(s), called the input weight, as shown in Figure 9.22. 

Variations on RBFNs have been developed that project the inputs on hyperellipses instead of hyperspheres. These ellipsoidal basis function networks allow nonunity and unequal input weights, except zero and negative values, causing elongation and contraction of the spherical receptive fields into ellipsoidal receptive fields (Kavuri and Venkatasubramanian, 1993). 

In order to prepare the final multi-class predictor map, the input weighted layers are fused using various Fuzzy operators (Fig. 5). Figure 6 is a reclassified final Fuzzy map, predicting the high potential areas for further drilling at East-Kahang. To validate the accuracy of the Fuzzy model, the projected Cu values of the completed drill holes are overlain on the final predictive map. The results show... 

Different linear combinations of the inputs (defined by the input weights, W) are then calculated to produce intermediate values (H1-H3), which are located in the hidden layer of the network. Within the hidden layer, these intermediate values are operated on by a transfer function (f) to produce processed intermediate values (Hl -H3 )- Then, a linear combination of these processed intermediate values (defined by the output weights W2) is calculated to produce an output value (01), which resides in the output layer of the network. In the context of analytical chemistry, the output (01) refers to the property of interest. 

For k output nodes, where Oj is the output from unit i and netj is the input (weighted sum of inputs) into unit i. 

Essentially, the neurofuzzy architecture is a neural network with two additional layers for fuzzification and defuzzification. The fuzzification and input weighting are illustrated graphically in Fig. 9, adapted from the thesis of Bossley. It can be seen that there are similarities with the RBF network, except now the radial functions are replaced by the multivariate membership functions. 

Figure 14.3 A comparison of learning episodes under varying initial orders and varying input weights...

Sidnraction can be performed by the circuit in l i.e-nre 4-lfib by introducing an inverter with R. R. in series wiili one or more of the resistors, thus clianging the sign of one or more ot the inputs. Weighted sub-... 

Step 2 Initialize the harmony memory. The HM is generated randomly, and each HM member (solution vector), v, represents a distinct feasible solution of all decision variables. That is, v = [v, v, . .., vj. The decision variables are composed of all input weights and hidden biases. 

This is a useful expression, since T can be expressed in terms of W, the PLS input weights, without having to break down X into its residuals for each latent dimension. A matrix of linear inner model regression parameters on the diagonal and zero-values off the diagonal, B can now be defined. Equations (5) and (4) can further be used to obtain... 

The equivalent linear programming problem is presented below, (the output and input weights are indicated as (p, v)). 

Test qualification of items in seismic categories 1 and 3 should be carried out when failure modes cannot be identified or defined by means of analysis or earthquake experience. Direct qualification by testing makes use of type approval and acceptance tests. Low impedance (dynamic characteristic) tests should be limited to identify similarity or to verify analytical models. Code verification tests should be used for the generic verification of analytical procedures, which typically use computer codes. The methods of testing depend on the required input, weight, size, configuration and operational characteristics of the item, plus the characteristics of the available test fadlify. 

The counterpropagation network-was originallyproposedby Hecht-Nilsen (1987). In this section a modified feedforward version as described by Zurada (1992) is discussed. This network, which is shown in Fig. 19.25, requires numbers of hidden neurons equal to the number of input patterns, or more exactly, to the number of input clusters. The first layer is known as the Kohonen layer with unipolar neurons. In this layer only one neuron, the winner, can be active. The second is the Grossberg outstar layer. The Kohonen layer can be trained in the unsupervised mode, but that need not be the case. When binary input patterns are considered, then the input weights must be exactly equal to the input patterns. In this case,... 

For injection molding machine, a minimum of three zone heating control of the cylinder is necessary for required temperature control. Although the thermal stability of LC melt is always high, the shortest melt residence time is adopted (less than 5 minutes) in the cylinder, i.e., the capacity of the machine almost matches with the input weight for the injection molding machine ( 50 to 75% of the machine s input capacity). 

THEN INPUT weight concentration in percent cwln... 

Now that we have the error, we are on familiar ground for updating all the weights coming into the neuron. We work out the change on each input weight using Equation 4.2. For this hidden neuron, our calculations would be as follows ... 

The nearer we get to the input, the smaller are the changes to the weights. Now that we have the change to each input neuron, we calculate the new value to weight with Equation 4.3. Using just the top input weight in Figure 4.8 (0.1), the example calculations would be as follows ... 

We then update all the other input weights to this layer and the training cycle is complete. This procedure is then repeated for the next training pattern until the entire training set has been submitted. 

Figure 9 Schematic of a neural network, (a) An individual neuron is characterized by its input weights and its output threshold, (b) Several neurons combined in a net serve to generate an output state from an input state vector. The input might be a sequence and the output a classification of that sequence according to its secondary structural propensity. A hidden layer in between input and output may further modulate the response...

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