Optimal Selection of Active Suspension Parameters Using Artificial Intelligence

optimal selection of active suspension parameters using artificial intelligence

in this paper, multi-objective uniform-diversity genetic algorithm (muga) with a diversity preserving mechanism called the ε-elimination algorithm is used for pareto optimization of 5-degree of freedom vehicle vibration model considering the five conflicting functions simultaneously. the important conflicting objective functions that have been considered in this work are, namely, vertical accel...

Optimal integrated passive/active design of the suspension system using iteration on the Lyapunov equations

In this paper, an iterative technique is proposed to solve linear integrated active/passive design problems. The optimality of active and passive parts leads to the nonlinear algebraic Riccati equation due to the active parameters and some associated additional Lyapunov equations due to the passive parameters. Rather than the solution of the nonlinear algebraic Riccati equation, it is proposed ...

Using Artificial Intelligence for Model Selection

We apply the optimization algorithm Adaptive Simulated Annealing (ASA) to the problem of analyzing data on a large population and selecting the best model to predict the probability that an individual with various traits will have a particular disease. We compare ASA with traditional forward and backward regression on computer simulated data. We find that the traditional methods of modeling are...

Efficient Parameters Selection for CNTFET Modelling Using Artificial Neural Networks

In this article different types of artificial neural networks (ANN) were used for CNTFET (carbon nanotube transistors) simulation. CNTFET is one of the most likely alternatives to silicon transistors due to its excellent electronic properties. In determining the accurate output drain current of CNTFET, time lapsed and accuracy of different simulation methods were compared. The training data for...

Artificial Intelligence Technique Selection