# Identification of Tire Model Parameters with Artificial Neural Networks
An interesting alternative for the identification of these coefficients is to use Genetic Algorithms (GA) and Neural Networks (NN). GA has been found to be good at exploring the parameter space [9; 10] and at estimating the MF coefficient, even starting from a bad estimation of the initial parameters. However, GA it is not very suitable for precise exploitation (i.e., identifying the optimum set of parameters). Recently, [PERSON] [11] has combined GA with a local search algorithm, proving that this hybrid two-step method is much more efficient. ANN has been successfully used along with state observers for dynamic control of cars [12; 13]. [PERSON] and [PERSON] [14] investigated some applications of the ANN to vehicle dynamics and control and compared it to the MF. They used tire test data to directly train the ANN and the results were promising. However, the starting data for the network training was very limited, as it was based on data from individual tires, of which there was also little data available for the network training. Regarding tire data obtained directly from the direct interaction of the tire with the road, [PERSON] [15] proposes a tool called TRICK to estimate the tire interaction forces as a function of slip rates, thus avoiding expensive and time consuming test bed characterizations. Later, [PERSON] [16] also proposed the use of another tool called TRIP-ID, for the interactive identification of tire parameters based on data from experimental bench data or direct interaction with the road. In the same way, [PERSON] [17] presented the identification of parameters through ANN for a specific tire, based on real test data of a race car tire (Hoosier 18.0 x 6-10R25B) on a test bench for different test conditions. In this line, in the evaluation of the parameters of [PERSON]'s MF model in the road operation of tires based on data acquisition, there is the problem of not having continuous data in the whole range and combinations of the slips. Therefore, it would be necessary to combine this identification of partially observable data in the vehicle dynamic system (with noisy and/or incomplete acquired data) with methodologies to reconstruct the complete model, as proposed by [18]. Unlike previous studies into ANN identifying tire parameters in MF, the present study is based on the evaluation of the possibility of creating a widely valid ANN, which can be trained to predict the parameters of [PERSON]'s MF, for a wide range of real tires. For this, a sufficiently ample database of curves is artificially created. Unlike previous ANN works, limited training data (due to data from only one, or a limited set of tires) is not a problem, since the methodology provides means to create as much training data as is necessary to ensure good network training. Based on this information, the ANN topology is selected and its performance is evaluated. Therefore, this article presents a novel methodology to create a \"universal\" ANN-based mathematical model, which can be trained before knowing the specific data obtained from bench tests, or from the acquisition of data of the tire-road contact. Furthermore, this ANN can consider all the specific variations that the Pacejka curve admits, being therefore valid to represent any of the effects (pure-slip, combined-slip, camber angle, pressure, temperature, humidity, etc.) that make such a mathematical model vary. Obviously, since it is based on a mathematical model that may not be perfect (in the sense that it may not be exactly faithful to reality), the ANN obtained will have at least the same errors as the model it represents (in this case [PERSON]'s MF). The advantage of having a pre-trained network that can represent a wide variety of conditions for different types of tires and immediately identify their Pacejka parameters is evident when you consider that the run time of such a network is only a few milliseconds.