We consider the problem of modeling the mean function in regression. Often there is enough knowledge to model some components of the mean function parametrically. But for other vague and/or nuisance ...

This article reviews work on the planning of experiments with response functions nonlinear in some parameters. Apart from older work on dilution series experiments and quantal bioassays, this field is ...

We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate ...

Author Dr. R. Russell Rhinehart discusses his new book, Nonlinear Model-Based Control: Using First-Principles Models in Process Control, and explains why nonlinear first-principles models should be ...

A pruning reservoir computing technique can mitigate noise and reconstruct nonlinear dynamics for potential engineering and neuroscience applications.

Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can ...

Highly reduced order models with nonlinear contact – extremely efficient computation. Modeled as linear components interconnected with highly reduced-order components. 1-linear reduced model, ...

In today's machine learning field, deep neural network models are becoming increasingly large and complex, posing significant challenges to traditional electronic computing hardware. To address this ...

Recognizing the nonlinear behavior of the 12AX7 vacuum tube, used in audio and old op-amp applications, Dr. Dyer compares various models for fit to real-world device data. Given appropriate models for ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy ...