Sentence examples for between the input vector from inspiring English sources

Using Eq. (1), we can measure the amount of similarity between the input vector and the stored pattern in TMC arrays.

y 0 (y + 0 and y − 0) is the amount of similarity between the input vector and the first column in the crossbar.

Equation 1 is based on the exclusive NOR function that can calculate the amount of similarity between the input vector and the stored data [10].

Taking the mean of this equation and applying the orthogonality principle between the input vector and the Wiener error, we obtain: E V n + 1 = E V n × 1 − 2 μ R ZZ. (56).

Taking the mean of this equation and applying the orthogonality principle between the input vector and the Wiener error, we get: E V n + 1 = E V n 1 − 2 μ R XX (33).

Hidden layers (n) act as intermediate layers between the input vector layer and output layer.

The kernel does not need to be strictly a matrix of cross-products between the input vectors.

The kernel function that best represents the true similarity between the input vectors will yield the best results, and kernel functions that poorly discriminate between similar and dissimilar input vectors will yield poor results.

Figure 6 shows the output of the GRBF kernel as a function of the distance between the input vectors for several different values of σ. Figure 7 shows the output of the ERBF kernel.

Given the mechanics of a SOM, one would not expect r m to have a significant effect on the results, since the organization of the map should merely depend on the relative distance between the input vectors.

In this article, we use the RBF kernel, since it can handle the case when the relation between class labels and the input vector is nonlinear as well as linear.