In physics, mathematics, economics, engineering, and many other fields, differential equations describe a function in terms of the derivatives of the variables. Put simply, when the rate of change of a variable in terms of other variables is involved, you will likely find a differential equation. Many examples describe these relationships. A differential equation’s solution is typically […]
Determining optimal control settings for an industrial process can be challenging. For example, when there are interactions between the effects of the controls, adjusting one setting can require readjusting other settings. This article addresses the problem using genetic optimization. Continue reading…
While it is straightforward to invert a function like y = mx to produce the inverse, x = y/m, some functions can’t be easily inverted. One such function is the cumulative distribution function (CDF) of the normal probability distribution, where neither the CDF nor the inverse CDF (quantile function) can be expressed in a closed form. This article […]
This article presents a method for training a neural network to derive the integral of a function. The technique works not only with analytically-solvable integrals but also with integrals that do not have a closed-form solution and are typically solved by numerical methods. An example is the normal distribution’s cumulative density function (CDF). Continue reading
Many relationships in physics, biology, chemistry, economics, engineering, etc., are defined by differential equations. In general, a differential equation (DE) describes how variables are affected by the rate of change of other variables. For instance, a DE explains how the position of a mass vibrating on spring changes with time in relation to the mass’s […]
Training With a Digital Twin Part 1 described a reinforcement learning system used to find the optimal control settings for a reflow oven used for soldering electronic components to a circuit board. Part 2 presents the details of the oven simulator used to accelerate the training process. Continue reading…
Part 1 explores the ability of a model trained with reinforcement learning (RL) to generalize, i.e., produce acceptable results when presented with data it was not exposed to during training. The application in this study is an industrial process with multiple controls that determine the effect on a product as it transitions through the process. […]
A previous post describes a reinforcement learning model trained to find the optimal control settings for a reflow oven that solders electronic components to a circuit board. The oven’s moving belt transports the product (i.e., the circuit board) through multiple heating zones. This process heats the product according to a temperature-time target profile required to […]
Can Reinforcement Learning generalize beyond its training? This paper explores the ability of a model trained with reinforcement learning (RL) to generalize, i.e., produce acceptable results when presented with data it was not exposed to during training. The application in this study is an industrial process with multiple controls that determine the effect on a […]
Tuning a Process Oven with Reinforcement Learning Determining optimal control settings for an industrial process can be tough. For instance, controls can interact, where adjusting one setting requires readjustment of other settings. Also, the relationship between a control and its effect can be very complex. Such complications can be challenging for optimizing a process. This […]