Abstract
In this paper, a Proportional–Integral–Derivative (PID) controller is fine-tuned through the use of artificial neural networks and evolutionary algorithms. In particular, PID’s coefficients are adjusted on line using a multi-layer. In this paper, we used a feed forward multi-layer perceptron. There was one hidden layer, activation functions were sigmoid functions and weights of network were optimized using a genetic algorithm. The data for validation was derived from a desired results of system. In this paper, we used genetic algorithm, which is one type of evolutionary algorithm. The proposed methodology was evaluated against other well-known techniques of PID parameter tuning.
Highlights
Optimization is a set of methods and techniques that is used to achieve the minimum and maximum values of mathematical functions, including linear and nonlinear functions
Optimization methods are divided in two forms: evolutionary separation methods and gradient-based methods
We introduce a genetic algorithm optimization method that can be applied to a neural network and Fuzzy nerve training
Summary
Optimization is a set of methods and techniques that is used to achieve the minimum and maximum values of mathematical functions, including linear and nonlinear functions. The first simulation efforts were conducted by Mac Klvk and Walter Pitts using the logical model of neuronal function that has formed the basic building blocks of most of today’s artificial neural networks [1,2,3,4]. The performance of this model is based on inputs and outputs. If the sum of entries is greater than the threshold value, the so-called neurons will be stimulated The result of this model was the implementation of simple functions such as AND and OR [5].
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