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Jaco
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I'm currently doing a project on neural network controller for an internal combustion engine to reduce emissions level. Can anyone assist me in training such models (neural networks)
Mech_Engineer said:What's the advantage of using a neural network in this situation? It seems to me a standard PID loop is perfectly sufficient...
russ_watters said:Most halfway decent PID controllers are self-tuing. Your car already does this.
For starters, by keeping track of operating parameters and matching the next one to the previously logged parameters. How much further it goes beyond that, I'm not sure, but a home thermostat does similar things. It keeps track of overshoot and adjusts the timings to compensate.Cyrus said:Cars are self-tuning, how do they do this?
http://en.wikipedia.org/wiki/Programmable_thermostatDigital thermostats with PID controller
More expensive models have a built-in PID controller, so that the thermostat knows ahead how the system will react to its commands. For instance, setting it up that temperature in the morning at 7am should be 21 degrees, makes sure that at that time the temperature will be 21 degrees (a conventional thermostat would just start working at that time). The PID controller decides at what time the system should be activated in order to reach the desired temperature at the desired time. It knows this by remembering the past behavior of the room, and the current temperature of the room.
It also makes sure that the temperature is very stable (for instance, by reducing overshoots at the end of the heating cycle) so that the comfort level is increased.
So, first off, rolling out the official welcome mat: Welcome to PhysicsForums, Jaco!Jaco said:I'm currently doing a project on neural network controller for an internal combustion engine to reduce emissions level. Can anyone assist me in training such models (neural networks)
A neural network controller for internal combustion engine is a type of artificial intelligence system that uses a network of interconnected nodes to simulate the decision-making process of the human brain. It is designed to optimize the performance of an internal combustion engine by learning from data and making adjustments to various parameters in real-time.
A neural network controller for internal combustion engine works by taking input from various sensors that monitor engine performance, such as temperature, pressure, and fuel consumption. This data is then fed into the neural network, which uses algorithms to analyze the data and make decisions on how to adjust engine parameters, such as fuel injection timing and air-fuel ratio, to optimize performance.
There are several benefits to using a neural network controller for internal combustion engine. Firstly, it can improve engine performance and efficiency by constantly adjusting parameters in real-time. Secondly, it can adapt to changing conditions and learn from past data, making it more accurate and efficient over time. Additionally, it can reduce emissions and extend the lifespan of the engine by optimizing its operation.
While a neural network controller for internal combustion engine has many benefits, it also has some limitations. One of the main challenges is the need for large amounts of data to train the neural network, which can be time-consuming and costly. Additionally, the complexity of the system can make it difficult to understand and troubleshoot issues if they arise.
Yes, neural network controllers for internal combustion engines are becoming increasingly popular in the automotive industry. They are used in a variety of applications, including optimizing engine performance, controlling emissions, and improving fuel efficiency. However, their use is still relatively new and there is ongoing research and development in this area to further improve their effectiveness and reliability.