Learn what linear and nonlinear models are, how they differ, and how they are applied in neural networks with real-world examples.

The graph-adaptive localized activation functions are implemented in terms of a nonlinear operator, employed within the one-hop neighborhood of each node. Specifically, we employ the max and median ...

For this aim, we try to obtain the best approximation of a nonlinear function as a piecewise linear function. Our method is based on an optimization problem. The optimal solution of this optimization ...

Home | Mathematics | Linear functions, graphs and equations | Graphing linear functions Graphing linear functions This section focuses on the key features and methods for working with linear graphs.

Graph wavelet transforms allow for the effective representation of signals that are defined over irregular domains. The transform coefficients should be sparse, and encode salient features of a signal ...

Discover new examples of non-elementary expo-elliptic functions and their applications in second-order nonlinear ODEs and systems of nonlinear ODEs. Explore Abel's methods and gain insights from ...

While nonlinear scalings of Lyapunov functions are also Lyapunov functions, we provide examples that the same statement does not necessarily hold for Input-to-State Stable (ISS) Lyapunov functions or ...

They can then plot this data on a graph and assess whether the function is linear or nonlinear. 3. Modelling: Modelling is an activity that encourages students to create mathematical models that ...